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Social Science & Medicine 55 (2002) 1015–1024
The spatial epidemiology of cholera in an endemic area ofBangladesh
Mohammad Alia,*, Michael Emchb, J.P. Donnayc, Mohammad Yunusd,R.B. Sacke
a International Vaccine Institute, Seoul, South KoreabDepartment of Geography, Portland State University, USA
cDepartment of Geomatics, University of Liege, Belgiumd ICDDR, B: Centre for Health and Population Research, Bangladesh
eJohns Hopkins University, USA
Abstract
This paper defines high-risk areas of cholera based on environmental risk factors of the disease in an endemic area of
Bangladesh. The risk factors include proximity to surface water, high population density, and low educational status,
which were identified in an earlier study by the authors. Cholera data were analyzed by spatially referenced extended
household units for two time periods, 1983–1987 and 1992–1996. These periods were chosen because they had different
dominant cholera agents. From 1983–1987 classical cholera was dominant and from 1992–1996 El Tor was dominant.
By defining high-risk areas based on risk factors, this study builds a spatial risk model for cholera. The model is then
evaluated based on the locations of observed cholera cases. The study also identifies the determinants of death due to
cholera for the two different time periods dominated by the different cholera agents. The modeled risk areas that were
based on the risk factors were found to correspond with actual distributions of cholera morbidity and mortality. The
high-risk areas of the dominant cholera agents are relatively stable over time. However, from 1983–1987 El Tor cholera,
which was not the dominant agent during that period, was not associated with high-risk areas, suggesting that the El
Tor habitat may have changed over time. The case fatality rate for cholera was related to proximity to a diarrhea
treatment hospital in the study area. r 2002 Published by Elsevier Science Ltd.
Keywords: Cholera; Disease risk; Spatial analysis; Bangladesh
Introduction and Background
An important part of health-needs assessment is the
identification of high-risk areas for a disease because
understanding the characteristics of high-risk areas may
indicate what is needed to improve health care provision
(Haining, 1996). However, most epidemiological studies
overlook the spatial components of disease and focus
solely on characteristics of the people who contract a
disease (Smoyer, 1998). While these studies are useful for
identifying biological factors of a disease, they usually
cannot establish accurate individual exposure levels for
the critical risk factors of the disease (Haining, 1998).
The incorporation of spatial components in health
studies facilitates the identification of high-risk disease
areas, the identification of sources of disease, the
definition of high-risk populations, and the optimal
allocation of health facilities (Jacquez, 2000). Identifying
high- and low-risk areas can help with the estimation of
resources needed for effective health planning and it may
help determine the underlying processes responsible for
the spatial patterns of disease.
Methods for identifying high-risk areas by disease
clustering techniques are well established (Myaux, Ali,
Chakraborty, & de Francisco, 1997a; Kulldorff &
Nagarwalla, 1995; Besag & Newell, 1991). However,
*Corresponding author. International Vaccine Institute,
Kwanak PO Box 14, Seoul, South Korea 1S1-600.
E-mail address: mali@ivi.int (M. Ali).
0277-9536/02/$ - see front matter r 2002 Published by Elsevier Science Ltd.
PII: S 0 2 7 7 - 9 5 3 6 ( 0 1 ) 0 0 2 3 0 - 1
defining high-risk areas based on case incidence may be
influenced by the size and age structure of the
population. Several methods, such as kernel estimation
and k-function have been proposed to remove such bias
in identifying clusters of disease (Gatrell, Bailey, Diggle,
& Rowlingson, 1996). Since socio-economic status varies
across households, defining risk areas based on house-
hold level case incidence may be influenced by the status
of the households. Another potential problem of using
disease incidence to define risk areas is the availability of
data sets. In developing countries, disease incidence
monitoring systems are not likely to be implemented on
a large scale because of resource constraints.
In this study high-risk areas, presumed to be niches of
cholera, are defined based on environmental-risk factors
of the disease. The risk factors are proximity to surface
water, high-population density, and low-educational
status, which were identified in an earlier study by the
authors (Ali, Emch, Donnay, Yunus, & Sack, 2001). An
environmental niche can be defined as a region that is
characterized by a set of environmental variables
(Gatrell, 1983). The combination of factors that permits
a species to survive defines its niche. By defining high-risk
areas based on risk factors, this study builds a spatial-
risk model for cholera. The model is then evaluated
based on the locations of observed cholera cases.
Different cholera agents coexist in Bangladesh but only
one agent is dominant at any one time. This study
compares the spatial distributions of the different cholera
agents at different times with the modeled risk areas. This
study also identifies the determinants of death due to
cholera for the two different time periods dominated by
different cholera agents. The determinants that are
investigated include accessibility to treatment centers,
educational level, and whether or not a person lives in a
flood-controlled area. Accessibility to treatment centers
has been found to be an important determinant of
diarrhea-related mortality (Rahman, Aziz, Munshi,
Patwari, & Rahman, 1982). Myaux, Ali, Felsenstein,
Chakraborty and de Francisco (1997b) reported that
acute watery diarrheal mortality for children under five,
although not statistically significant, was higher inside a
flood-protected area than outside. Educational level is
hypothesized to be an indicator of mortality because it
indirectly determines healthcare seeking behavior.
The study area and data sources
The study area
This study was conducted in Matlab, a rural area of
Bangladesh with endemic cholera. It is 53 km southeast
of Dhaka, the capital of Bangladesh. The study area is in
the central plain of Bangladesh adjacent to the
confluence of the Meghna and the Ganges Rivers. The
Dhonagoda River bisects the study area into two
approximately equal parts. There are also numerous
canals that remain dry in the winter and fill with water
during the monsoon. In the late 1980s, a flood-control
embankment was built along the Dhonagoda and
Meghna Rivers. The embankment was built primarily
to protect the area from monsoon flooding so that crops
can be grown throughout the year. It protects 31% of
the study population from flooding. The people of the
study area live in groups of patrilineally related house-
holds called baris. An average of six households
constitute a bari.
The spatial database
A vector spatial database of the study area was created
in 1994 to facilitate spatial analysis in health and
population research. Features in the database that were
used in this study include baris, rivers, and the embank-
ment (Fig. 1). The spatial database was converted to the
raster format in which space is divided into discrete units
called pixels. The size of the pixels was set to 30 m, which
facilitates the representation of baris by single pixels. A
total of 7691 pixels represent the baris and each pixel has
a unique identification number so that health and
population data can be linked to bari locations.
Health and demographic databases
Individual health and demographic surveillance data
are regularly collected for all individuals living in the
Matlab study area. These individual level data
were aggregated by baris. Cholera cases were identified
from Matlab hospital surveillance records. The Matlab
hospital is the only diarrhea treatment center in this
rural area and it provides free treatment to all patients.
Therefore, it is unlikely that a diarrhea patient from
the area would miss the opportunity to receive
treatment. Stool samples are collected for all patients
who live in the study area, and the samples are screened
for enteric pathogens in the laboratory. Since the
cholera morbidity data were based on hospitalized
patients, the analysis was restricted to the baris located
within 9 km of the hospital because few patients
reported to the hospital from further away. Cholera
morbidity data were collected for two time-periods,
1983–1987 and 1992–1996, in order to understand the
spatial epidemiology of two different agents of cholera,
classical and El Tor. Classical cholera was the dominant
agent from 1983–1987 and El Tor cholera from 1992–
1996. From 1983–1987 there were 1236 classical cholera
cases and 719 El Tor cholera cases. A total of 1342 El
Tor cholera cases were identified from 1992–1996 and no
classical cholera cases were identified during that time
period.
M. Ali et al. / Social Science & Medicine 55 (2002) 1015–10241016
Medical assistants regularly report the cause of death
for all people living in the Matlab study area. Data on
deaths due to acute watery diarrhea (AWD), also
called cholera-like diarrhea, were collected from the
demographic database. Medical assistants assigned
causes of deaths by reviewing verbal autopsy forms
recorded using methods described elsewhere (Kielmann,
DeSweemer, Parker, & Taylor, 1983). The classification
of the causes of death was derived from the Interna-
tional Statistical Classification of Diseases, Injuries and
Causes of Death (WHO, 1977), and was adjusted
according to the Matlab surveillance reporting system.
The mortality data were aggregated by baris for the two
time-periods by summing total deaths for each time-
period.
Methods
Cholera morbidity and AWD mortality indices
This study defines cholera-risk areas based on three
risk factors. It also assesses how well the risk areas
correspond to the actual distributions of cholera
morbidity and AWD mortality. Cholera-morbidity and
AWD-mortality indices were calculated within the raster
Fig. 1. Vector spatial database of the study area.
M. Ali et al. / Social Science & Medicine 55 (2002) 1015–1024 1017
GIS (Ali et al., 2000). The rates were calculated by
using a spatial smoothing technique referred to as a
spatial moving average rate (Kafadar, 1996). A 7� 7-
pixel moving window was used to compute average
rates for observed bari points (Talbot, Kulldroff, Teven,
& Haley, 2000). The expression for computing the
indices is
jDi ¼
Pnj¼1 dj�kjPnj¼1 pj�kj
�1000;
where i refers to image pixel, and j refers to window
pixel. Therefore, ji=rates per 1000 per year in bari i;dj=number of cases in bari j; pj=total number of
people in bari j; kj=kernel values (unitary) of cell j of the
moving window.
Indices of cholera morbidity and AWD mortality
were computed for the two time periods. The ratios were
multiplied by 1000 to express the index (jDi) per 1000
people.
Defining high-risk areas
Since there were multiple risk factors and each
was responsible for different levels of risk, a multi-
criteria evaluation model (Voogd, 1983; Carver, 1991)
was built to create a single index of risk. The single
value of risk is the linear combination of the
factors multiplied by its weight. The index of risk is
expressed as
Ri ¼ Sxijwj ;
where, Ri=spatial risk for bari i; xij=standardized score
of factor j of bari i; wj=weight of factor j:Since the factors were measured in different units,
standardized scores of the factors were used. The
evaluation model requires that higher values be
positively correlated with the outcome. Therefore,
the standardized scores of the negatively correlated
factors (distance to surface water and educational
status) were reversed. The factors’ weights were
determined by the relative importance of the factors in
influencing disease incidence, which were derived
from the percentage change in an outcome variable
associated with change in a risk factor. The regression
model used to determine the risk factors from 1992–1996
was used in this process. The risk factors were the same
for both time periods, although the magnitude of
influence on the outcome variable was different. The
factors’ weights were calculated by taking the principal
eigen vector of the squared reciprocal matrix of pairwise
comparisons describing the relative importance of the
factors (Saaty, 1977). The spatial risk for the observed
locations was computed for each of the two time
periods.
Kriging was used to interpolate spatial risk at a
regularly spaced interval in space in order to map risk
areas. Kriging is one of the most flexible methods for
interpolating data at unsampled points (Collins, 1998).
Its estimates are unbiased and have a known minimum
variance (Oliver & Webster, 1990). The interpolated
values at unsampled points were computed using the
following formula:
Gi ¼Xn
i¼1
lijZj ;
where, Gi=interpolated data at point i; n=number of
data points used in interpolating the data, Zj ¼ Z value
at the jth data point, lij=weight associated with the jth
data for computing Gi
The weighting factor lij varies between 0 and 1 in a
continuous scale; the closer a data point is to a measured
point, the more weight it will carry. The sum of the
weighting factors used to calculate the value is 1. In
defining the grid-cells, spacing was set to 150 m in all
directions. Kriging was found to be so robust that even
when spacing was set to 300 m, little difference was seen
in the resulting maps. Contour mapping methods, which
used the krigged data, were used to define the surface in
low-, moderate-, and high-risk areas. The thresholds to
define the risk were based on the distribution of the
single value of risk. Values below the mean were
considered to be low risk; values between the mean
and the mean plus half of the standard deviation were
considered to be moderate; and those higher than that
were considered to be high risk.
In the previous study (Ali et al., 2000) that identified
the three risk factors used to map cholera risk, proximity
to surface water was calculated using two different
methods. One method used the vector GIS database
to identify the main rivers and canals and the other
used Landsat Thematic Mapper satellite imagery to
identify all surface water that can be represented in
a 30-m resolution satellite image. The two data sources
were used in the analytical stage of this project because
they represent slightly different variables. The resulting
map of the risk areas, defined by using the main rivers
and canals variable as well as population density
and socio-economic status, is shown in Fig. 2. This
group of three variables including the main rivers and
canals is hereafter referred to as Model 1. The Model 1
risk map resulted in 78.67 km2 in the low-risk category,
59.18 km2 in the moderate risk category, and 46.26 km2
in the high-risk category. Fig. 3 represents the risk areas
obtained by using surface water derived from satellite
data as well as population density and socio-economic
status. This group of three variables including the
surface water derived from satellite imagery is hereafter
referred to as Model 2. The Model 2 risk map resulted in
77.98 km2 in the low-risk category, 55.06 km2 in the
moderate risk category, and 51.08 km2 in the high-risk
category.
M. Ali et al. / Social Science & Medicine 55 (2002) 1015–10241018
Assessment of risk areas
Spatial agreement between risk models
This study presents two different models of cholera
risk. It is therefore useful to know how the models agree
with each other in defining the risk areas. A k-statistic
was calculated to measure the spatial agreement between
the two models (Cohen, 1960). There was moderate
agreement between the moderate risk areas (k=0.48) for
the two models and good agreement between the high-
(k=0.67) and low-risk areas (k=0.72) (1=perfect
agreement; >0.6=good agreement; 0=no agreement).
Spearman’s correlation coefficients were also calculated
to measure agreement between the risk categories for the
two models. The results show that there is strong
agreement between the models (rs ¼ 0:75) suggesting
that the two measures of proximity to surface water are
possibly interchangeable. These results are expected
because only one factor between the models differs.
Risk areas and cholera morbidity
Cholera morbidity prevalence rates were calculated
for each of the three risk categories. The analysis was
done for both time periods using both data models.
From 1992–1996 there were 6012 baris within 9 km of
the hospital and from 1983–1987 there were 4399 baris
within 9 km of the hospital. The distribution of the baris
by risk area for each of the time periods is presented in
Table 1. Linear and logistic regression models were built
to determine whether cholera prevalence rates were
significantly different for the areas of different risk
categories. For instance, did the high-risk category have
Fig. 2. Map of the risk areas defined by Model 1.
M. Ali et al. / Social Science & Medicine 55 (2002) 1015–1024 1019
significantly higher cholera morbidity than the moder-
ate- or low-risk areas? In the linear-regression model, the
outcome variable was the spatially smoothed cholera-
morbidity rate, and in the logistic-regression model, the
outcome was defined by presence or absence of cholera
in a bari. In both of the regression models, the risk
categories were incorporated as dummy variables. Since
there are three risk categories, two binary dummy
variables were created, one for moderate risk and the
other for high risk. Table 2 shows the results of the
regression models, which indicate that the high-risk
areas had significantly higher cholera morbidity in each
of the models. The analysis was restricted to within 9 km
from the hospital. The table shows that the El Tor
prevalence rates from 1992–1996 is 22% higher in
moderate-risk areas and 24% higher in high-risk areas
compared to the rate in the low-risk areas as defined by
Model 1. Model 2 shows that the El Tor prevalence rate
is only 7% higher (not statistically significant) in
moderate-risk areas and 20% higher in the high-risk
areas compared to the rate in the low-risk areas. On the
other hand, from 1983–1987 the classical cholera cases
are significantly higher in the high-risk areas compared
to the rates in the low-risk areas. Table 2 also shows that
the findings of the logistic-regression analyses are the
same as the findings of the linear-regression analyses.
Risk areas and AWD mortality
The AWD mortality rates were assessed by risk
category and relationships between several predictor
variables and AWD mortality were measured. All active
baris were studied because distance from the hospital
does not affect the mortality data since it is community-
Fig. 3. Map of the risk areas defined by Model 2.
M. Ali et al. / Social Science & Medicine 55 (2002) 1015–10241020
level data. There were 7467 baris from 1992–1996 in the
study area and 5718 baris from 1983–1987. The
distribution of the baris by risk area for each time
period is presented in Table 1. From 1992 to 1996 there
were approximately 3 AWD deaths per 10,000 people
per year. From 1983 to 1987 there were approximately 6
AWD deaths per 10,000 people per year. As with the
morbidity data, the mortality rate is significantly higher
in the high-risk areas for both periods (Table 3).
AWD mortality and predictor variables
Accessibility to treatment centers is an important
determinant of diarrhea mortality (Rahman et al., 1982).
In this study the cost distance to the nearest treatment
center was used to model accessibility. Movement in
space incurs a cost (time or money), which is a function
of frictions and forces that impede or facilitate move-
ment. Since there are numerous water bodies in the
study area, the linear distance would give imprecise time
costs for accessing the treatment centers in the study
area. Therefore, the cost distance was computed instead.
When calculating the time it takes to travel from
locations in the study area and the treatment centers,
traveling over water (rivers and canals) was assumed to
take 5 times more than traveling over the ground.
Myaux et al. (1997b) reported that AWD mortality for
children under five, although not statistically significant,
was higher inside the flood protection embankment. The
average number of years of education by bari was also
included in the list of the independent variables because
it indirectly determines healthcare seeking behavior.
Distance to the nearest treatment center significantly
influenced the mortality rate for both time periods
(Table 4). However, using the logistic model, there was
not a relationship between mortality and distance to a
treatment center. This might have resulted from using
continuous data with a binary outcome variable. From
Table 1
Frequency distribution of the baris by risk category
Categories of risk areas 1992–1996 1983–1987
Within 9 km from
hospital n ¼ 6012 no. (%)
Total study area
n ¼ 7467 no. (%)
Within 9 km from
hospital n ¼ 4399 no. (%)
Total study area
n ¼ 5718 no. (%)
Model 1a
low 2592 (43.1) 3037 (40.7) 1734 (39.4) 2143 (37.5)
moderate 1898 (31.6) 2268 (30.4) 1421 (32.3) 1760 (30.8)
high 1522 (25.3) 2162 (28.9) 1244 (28.3) 1815 (31.7)
Model 2a
low 2440 (40.6) 2985 (40.0) 1730 (39.3) 2236 (39.1)
moderate 1854 (30.8) 2212 (29.6) 1306 (29.7) 1629 (28.5)
high 1718 (28.6) 2270 (30.4) 1363 (31.0) 1853 (32.4)
a Please see the text for the definition of models.
Table 2
Regression analysis for risk area assessment: cholera morbidity
Categories of
risk areas
El Tor cholera, 1992–1996 Classical cholera, 1983–1987
Linear regression Logistic regression Linear regression Logistic regression
Rate/1000 % higher Odds ratio 95% CI Rate/1000 % higher Odds ratio 95% CI
Model 1a
Low 2.676 1 1.00 2.678 1 1.00
Moderate 3.274 22.3b 1.44 1.24–1.67b 2.942 9.9c 1.38 1.17–1.63b
High 3.317 24.0b 1.84 1.58–2.14b 3.332 24.4b 1.65 1.39–1.95b
Model 2a
Low 2.805 1 1.00 2.651 1 1.00
Moderate 2.997 6.9 1.20 1.03–1.40b 2.796 5.5 1.33 1.12–1.57b
High 3.375 20.3b 1.69 1.46–1.96b 3.474 31.0b 1.82 1.54–2.14b
a Please see the text for the definition of models.bpo0:01:cpo0:05:
M. Ali et al. / Social Science & Medicine 55 (2002) 1015–1024 1021
1992–1996 the area outside the flood-control embank-
ment had an 18% higher mortality rate compared to the
rate inside the embankment. The odds of dying were
1.53 (95% CI=1.18–1.99) times higher outside the
embankment than inside. The mean number of years
of education for each bari was 0.81. Level of education
was negatively related to mortality using both the linear-
and logistic-regression models for both time periods.
Discussion and Implications
Factors that put the people living in an area at risk for
cholera and AWD mortality have remained the same for
decades. The methods used in this study to define high-
risk areas are more robust than disease-clustering
methods based on case incidence rates. Defining high-
risk areas based on reported health events such as with
disease-clustering methods cannot be extrapolated to
other areas. Defining high-risk areas using risk factors
can be used in other areas where risk factor data are
available. However, when defining risk areas, the
different health outcomes used in this study (morbidity
and mortality) provide somewhat different risk surfaces.
This should be considered when planning the allocation
of services. This study provides new insight into the
spatial epidemiology of cholera by investigating the
spatial variation of the disease over time for two
different dominant agents of the disease. The high-risk
areas have persisted for decades for different dominant
cholera agents. This suggests that the niche for cholera is
in specific places in this endemic area. Higher morbidity
in the high-risk areas in both periods suggests that the
habitats of the present form of cholera, El Tor, and the
previous form, classical, are the same. Conversely, El
Tor risk areas during the two time periods are not the
same, which suggests that the present form of El Tor
cholera may have changed its environment and its
habitat is now the same as classical cholera before that
biotype disappeared.
The results show that only the dominant cholera
agents are associated with AWD mortality for both
Table 3
Regression analysis for risk area assessment: AWD mortality
Categories of risk areas AWD mortality, 1992–1996 AWD mortality, 1983–1987
Linear regression Logistic regression Linear regression Logistic regression
Rate/10,000 % higher Odds ratio 95% CI Rate/10,000 % higher Odds ratio 95% CI
Model 1a
Low 2.91 1 1.00 4.97 1 1.00
Moderate 2.76 �5.2 1.09 0.80–1.46 6.48 30.4b 1.52 1.21–1.92b
High 3.53 21.3c 1.74 1.33–2.29b 6.74 35.6b 1.82 1.45–2.27b
Model 2a
Low 2.90 1 1.00 4.96 1 1.00
Moderate 2.93 1.0 0.92 0.68–1.24 6.51 31.3b 1.41 1.12–1.78b
High 3.32 14.5d 1.38 1.06–1.81c 6.81 37.3b 1.73 1.39–2.15b
a Please see the text for the definition of models.bpo0:01:cpo0:05:dpo0:10:
Table 4
Regression analyses for independent variables: AWD mortality
Variables AWD Mortality, 1992–1996 AWD Mortality, 1983–1987
Linear regression Logistic regression Linear regression Logistic regression
ba p-value ba p-value ba p-value ba p-value
Level of education �5.418E-02 0.007 �0.2477 0.0467 �0.166 0.000 �0.4412 0.0000
Outside embankment 4.909E-02 0.025 0.4282 0.0012 �6.664E-02 0.073 �0.0903 0.3622
Distance to the nearest TC 6.376E-04 0.000 0.0006 0.4200 5.990E-04 0.005 0.0002 0.7589
a Coefficient.
M. Ali et al. / Social Science & Medicine 55 (2002) 1015–10241022
periods and that the location of high-risk areas is the
same for both time periods. In contrast, from 1983 to
1987 there was not a relationship between El Tor and
mortality. This suggests that the ecological state of the
high-risk areas might be involved in El Tor becoming a
more virulent agent. Therefore, it is imperative to
understand the aquatic environments of the high-risk
areas where the ecological conditions favor long-term
survival of cholera. Investigating the aquatic environ-
ment could reveal what ecological factors make Matlab
an endemic area and what changes have caused the
disappearance of classical cholera and increased the
virulence of El Tor.
Proximity to the treatment centers is a determinant of
AWD mortality as was found in a previous study
(Rahman et al., 1982). This suggests that access to
hospitals needs to be improved in the study area. Since a
cholera victim becomes dehydrated very rapidly, fast
access to health care facilities needs to be ensured. There
was also a relationship between educational level and
AWD mortality from 1983 to 1987. People with less
education are more likely to seek care from indigenous
medical practitioners, thus reducing the chance of
survival (Ali, Emch, Tofail, & Baqui, 2001). From
1992 to 1996 educational status did not influence
mortality as much as it did from 1983 to 1987. The
AWD mortality rate has also decreased over time.
Areas inside the embankment had higher cholera
morbidity than outside the embankment. Since the
embankment protects the area from flooding, there is
no longer natural flushing within the flood-protected
zone. The absence of natural flushing by floodwater has
changed the hydrological dynamics within the embank-
ment resulting in increased salinity (Siddique et al.,
1991). This has created a suitable environment for
cholera and therefore an increase in the disease
incidence. However, the higher morbidity did not result
in higher mortality inside the embankment. In contrast,
the embanked area had lower mortality compared to the
area outside the embankment. This could be due to the
effect of the proximity of baris to the nearest treatment
center. The average distance of the baris inside the
embankment to the nearest treatment center was greater
than those outside the embankment. Also, children who
are affected by enteric diseases are on average better
nourished inside the embankment (Myaux et al., 1997b)
and this may result in lower AWD mortality rates in the
area.
With advances in medical sciences, cholera is no
longer the deadly disease it used to be, but it is likely to
continue to cause concern and despair in Bangladesh
and other developing countries. This study shows that
geographical investigations are important in public
health for initiating effective control programs to reduce
health problems from environmental diseases. The water
and sewage infrastructure available in industrialized
countries will not be realized for some time in
developing countries. An intensive health intervention
program directed at high-risk areas in endemic areas will
assure that services are provided to the areas that need
them the most.
Acknowledgements
This research was funded by Belgian Administration
for Development Cooperation and ICDDR,B: Centre
for Health and Population Research. The ICDDR,B is
supported by countries and agencies which share its
concern for the health problems of developing countries.
Current donors providing unrestricted support include:
the aid agencies of the Governments of Australia,
Bangladesh, Belgium, Canada, Japan, the Netherlands,
Sweden, Sri Lanka, Switzerland, the United Kingdom
and the United States of America; international
organizations include United Nations Children’s Fund.
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