Spatiotemporal distribution and population at risk of soil-transmitted helminth infections following an eight-year school-based deworming programme in Burundi, 2007–2014
Assoum et al. Parasites & Vectors
Spatiotemporal distribution and population at risk of soil-transmitted helminth infections following an eight-year school- based deworming programme in Burundi, 2007-2014
Mohamad Assoum 0 1 2 3
Giuseppina Ortu 6
Maria-Gloria Basáñez 5
Colleen Lau 1 3 4
Archie C. A. Clements 4
Kate Halton 8
Alan Fenwick 7
Ricardo J. Soares Magalhães 0 1 3
0 UQ Spatial Epidemiology Laboratory, School of Veterinary Science, The University of Queensland (Gatton Campus) , Via Warrego Highway QLD, Gatton 4343 , Australia
1 Children's Health and Environment Program, Child Health Research Centre, The University of Queensland , Brisbane , Australia
2 School of Medicine, The University of Queensland , Brisbane , Australia
3 Children's Health and Environment Program, Child Health Research Centre, The University of Queensland , Brisbane , Australia
4 Research School of Population Health, Australian National University , Canberra , Australia
5 London Centre for Neglected Tropical Disease Research, Department of Infectious Disease Epidemiology, Faculty of Medicine (St. Mary's Campus), Imperial College London, School of Public Health , Norfolk Place W2 1PG, London , UK
6 Present address: Malaria Consortium Headquarters. Development House , 56-64 Leonard Street EC2A 4LT, London , UK
7 Schistosomiasis Control Initiative, Imperial College London, Department of Infectious Disease Epidemiology, School of Public Health, Faculty of Medicine (St. Mary's Campus) , Norfolk Place, W2 1PG, London , UK
8 Queensland University of Technology , Brisbane , Australia
Background: Investigating the effect of successive annual deworming rounds on the spatiotemporal distribution of infection prevalence and numbers at risk for soil-transmitted helminths (STHs) can help identify communities nearing elimination and those needing further interventions. In this study, we aim to quantify the impact of an 8-year mass drug administration (MDA) programme (from 2007 to 2014) on the spatiotemporal distribution of prevalence of STH infections and to estimate the number of school-aged children infected with STHs in Burundi. Methods: During annual longitudinal school-based surveys in Burundi between 2007 and 2011, STH infection and anthropometric data for a total of 40,656 children were collected; these data were supplemented with data from a national survey conducted in 2014. Bayesian model based geostatistics (MBG) were used to generate predictive prevalence maps for each STH species and year. The numbers of children at-risk of infection per district between 2008 and 2014 were estimated as the product of the predictive prevalence maps and population density maps. Results: Overall, the degree of spatial clustering of STH infections decreased between 2008 and 2011; in 2014 the geographical clusters of all STH infections reappeared. The reduction in prevalence was small for Ascaris lumbricoides and Trichuris trichiura in the centre and central north of the country. Our predictive prevalence maps for hookworm indicate a reduction in prevalence along the periphery of the country. The predicted number of children infected with any STH species decreased substantially between 2007 and 2011, but in 2014 there was an increase in the predicted number of children infected with A. lumbricoides and T. trichiura. In 2014, the districts with the highest predicted number of children infected with A. lumbricoides, T. trichiura and hookworms were Kibuye district (n = 128,903), Mabayi district (n = 35,302) and Kiremba (n = 87,511), respectively. Conclusions: While the MDA programme in Burundi resulted in a reduction in STH prevalence, this reduction was spatiotemporally heterogeneous, with some pockets of high prevalence remaining, suggesting that treatment coverage and complementary interventions should be evaluated to improve impact.
Spatiotemporal modelling; Soil-transmitted helminths; Predictive risk mapping; Number of infections
Soil-transmitted helminth (STH) infections are intestinal
nematode infections that affect approximately 1.6 billion
people around the world, with the majority of infections
occurring in resource-poor settings [
]. Since the signing
of the London Declaration on Neglected Tropical
Diseases (NTDs) in 2012, programmes for the control of
STH infections and other NTDs have received renewed
support from the pharmaceutical industry, the scientific
community and key development agencies and
]. Reductions in prevalence of infection and
associated morbidity can be achieved by successive mass
drug administration (MDA). More recently, it has been
argued that to further control and reach elimination
targets, MDA campaigns would need to be integrated with
water, sanitation and hygiene (WASH) programmes [
While MDA is seen as a cost-effective intervention to
achieve morbidity control, rapid re-infection means that it
can be ineffective at reducing transmission, especially for
populations living perennially in STH-contaminated
environments. Morbidity control through long-standing MDA
programmes can be undermined by geographical
disparities in drug coverage and drug efficacy and by
socioeconomic conditions that limit the access and adequate
utilisation of water and sanitation infrastructure [
The Schistosomiasis Control Initiative (SCI) has been
actively involved with the planning, implementation and
continued monitoring and evaluation of anthelmintic
MDA programmes using albendazole (ALB) and
mebendazole (MEB) in 16 sub-Saharan African (SSA) nations
including Burundi. From 2007 until 2014, SCI supported an
MDA programme in Burundi [
], primarily targeting
school-aged children (SAC) and pregnant women. In
2007, a pilot longitudinal study was launched in 12
schools, followed in 2008 by an extension study, in which
an additional 19 schools across the country were added
]. The longitudinal study aimed to assess the impact of
MDA on STH control in Burundi, and found that the
overall prevalence of STH infection was statistically
significantly reduced over the programme’s duration.
However, this investigation also found that programmatic
disruption (due to political and civil unrest in late 2009
through to 2010) resulted in substantially reduced levels
of treatment coverage for that period, with a consequent
detectable resurgence in STH prevalence. This highlighted
the pressing need for STH control interventions to not
rely solely on MDA, a strategy that may not be sustainable
in the long term and which crucially requires achieving
high levels of treatment coverage and adherence. This
notion has been supported by numerous other studies [
which indicate that in some endemic areas with high
transmission, high intensity of infection may persist,
requiring integration of MDA with WASH if elimination is
to be achieved.
Predictive prevalence mapping based on spatial models
that include environmental drivers of infection has been
widely used to identify areas in SSA where communities
are at highest risk of STH infection and thus deworming
campaigns should be targeted [
]. Most studies
have focused on estimating the spatial variation of
indirect morbidity indicators, such as prevalence and
intensity of infection [
]. In the case of Burundi,
predictive prevalence maps were produced in 2007 to
focus treatment delivery based on areas of high
uncertainty of high infection prevalence [
]. The study found
that predictive prevalence mapping was indeed an
effective tool for guiding MDA implementation to
maximise deworming efficiency [
]. However, the impact of
successive (annual) MDA rounds on the spatiotemporal
variation of prevalence of STH infections such as the
ensuing 8-year MDA programme in Burundi [
] has not
been investigated. In our previous study, we found that
disruption in the delivery of MDA, for example as a result
of social unrest, may have contributed to the observed
rebound in STH infection prevalence [
]. Furthermore, we
found that the most common co-infections noted were A.
lumbricoides and T. trichiura which peaked in 2008 at
2.72%. However rates of co-infections dropped
substantially over the course of the MDA, with coinfections
making up less than 2% per year following 2008. However,
the impact of the MDA programme on the spatiotemporal
distribution of prevalence of STH infection is largely
unknown, and this understanding may have implications for
the achievement of the overall intervention goal. Thus
mapping heterogeneity in prevalence of infection over
time is important, as it allows us to identify areas where
MDA has been systematically successful and, more
importantly, areas where it may have failed and where
further MDA campaigns may be needed.
In the present study, we aim to: (i) quantify the impact
of an 8-year MDA programme (from 2007 to 2014) on
the geographical distribution of STH infection
prevalence, and (ii) estimate the spatiotemporal variation in
the number of STH-infected children following the
8year programme. Our ultimate goal is to identify areas
in Burundi where the impact of MDA has been
systematically suboptimal at reducing prevalence and number of
infections; this will help support the planning of further
studies within these areas to understand the determinants
of programme coverage and efficacy. Furthermore, it will
also support the planning of further programmatic
Data collection on STH infection
The protocol for data collection for the 2007–2011
surveys has been reported elsewhere [
]. In brief, the 2007–
2011 surveys were conducted in conjunction with the
delivery of the MDA programme. Data collected
included child’s age, sex, height, weight, and parasite egg
count by STH species. Stool samples were taken from
100 children (approximately 50 boys and 50 girls) per
]; each year, samples were collected in May and
the MDA round was delivered in June. The diagnostic
approach using the Kato-Katz method was detailed in
our previous paper [
]. During the 2014 survey, similar
data collection protocols comparable to those of the
2008–2011 period were used [
]. In 2014, all 12 schools
from the pilot study plus 14 out of the 19 schools from
the extension study were re-assessed to evaluate the
prevalence and intensity of STH infection after 7 years
of annual MDA [
]. In each school in 2014, 50 pupils
aged between 12 and 16 years were recruited, with the
exception of one pilot study school in which 100 pupils
were recruited [
]. In the 2008–2011 cohort, students
were aged between 5 and 18 yrs. In 2014, the Ministry
of Health, with the support of the Schistosomiasis
Consortium for Operational Research and Evaluation
(SCORE), conducted a national survey. Further details
on the 2014 national survey have been reported
A single stool sample was collected from each child
and duplicate slides were prepared [
]. Diagnosis of STH
infection was performed using the Kato-Katz technique
by trained local ground staff [
]. If a single egg of a
given parasite species was found, the child was
considered positive for that parasite species. Egg counts were
used to detail the intensity of infection.
Geographical coordinates of each school were
recorded using hand-held global positioning system (GPS)
units. Overall prevalence of infection was calculated for
each school and for each parasite species. These summary
data were plotted in a geographical information system
(GIS) (ArcMap version 10.3, ESRI, Redlands, CA, USA).
Infection data were gathered and collected from the
same 31 schools during 3 years (2008, 2009 and 2011);
however, due to civil unrest, only 12 out of the 31 were
surveyed in 2010. In 2014, 26 out of the 31 schools were
surveyed due to staffing issues. A total of 40,656
children were sampled over the 8 years. For the 2014 survey,
height, weight and blood haemoglobin levels were not
Environmental and population data
Environmental influences on STH species, such as A.
lumbricoides and T. trichiura, are well known. Land
surface temperature (LST), soil type, and distance to a
water bodies influence the survival of parasite eggs in
the environment, and therefore determine the intensity
of exposure [
]. Equally, the transmission of hookworm
species is determined by climate and landscape, as their
larvae burrow into the soil to survive in more favourable
]. Electronic data for a
normalised difference vegetation index (NDVI) for a 30 × 30 m
grid cell resolution were obtained from LandSAT 5 and
8 satellite images via the Google Earth Engine (GEE)
database (Additional file 1: Table S1). Elevation data with
a 30 × 30 m grid resolution, generated by a digital
elevation model (DEM) from the Advanced Space-borne
Thermal Emission and Reflection Radiometer (ASTER)
Global Digital Elevation Model (GDEM), were obtained.
LST data were also obtained from the ASTER system
with a 500 × 500 m resolution. Precipitation data were
collected from WorldClim with 1 × 1 km grid
resolution. Remotely-sensed data for LST and NDVI were
recorded monthly from 2007 to 2014 and a new annual
raster file was created. The locations of large perennial
inland water bodies were obtained from the Food and
Agriculture Organization of the United Nations [
and the distance to perennial inland water bodies
(DPWB) was estimated for each survey location in the
GIS. A 5 × 5 km resolution population density surface
derived from the Global Rural-Urban Mapping Project
(GRUMP) beta product was obtained from the Centre
for International Earth Science Information Network
(CIESIN) of the Earth Institute at Columbia University
]. Values at each survey location for all environmental
datasets were extracted in the GIS.
Non-spatial models of STH infection
We assessed the temporal variation in environmental
variables between 2007 and 2011, and it was found that
the environmental variables did not vary significantly
between years. As such, only the 2011 values were used
for analyses (Additional file 1: Table S2). The
relationship between the prevalence of infection with each
parasite for each of the 31 schools and the arithmetic mean
of each environmental variable at the school location
was evaluated using scatter plots and lines of best fit. If
the relationship was found to be linear, then the variable
was included in the univariable and multivariable
analysis as a fixed effect. Non-linear relationships were
explored using linear regression; however, we did not
consider any transformation for our final models. To
identity the best set of uncorrelated predictor
environmental covariates, the Pearson’s correlation coefficient
was calculated for all pairs of environmental variables at
all data locations for all years.
Fixed-effects binomial logistic regression models of
prevalence of infection for each STH parasite species
were developed in Stata version 10.1 (Stata Corporation,
College Station, TX, USA). All univariable models
included the individual-level variables age and sex as fixed
effects and environmental covariates including either
NDVI, LST, precipitation, DPWB or elevation. In the
univariable analysis, Wald’s P-value of 0.2 was used to
select variables to be included in the final multivariable
models for each parasite species. Multivariable analysis
was conducted including age and sex as fixed effects in
the models and all selected environmental variables as
fixed effects. Using a backward stepwise process of
variable selection, variables with a P-value greater than 0.05
were excluded from the final multivariable model.
However, if the coefficient of a given variable changed by
more than one quarter of the value of the model preceding,
due to the removal of the variable, then the removed
variable was deemed to be a confounder and was retained in
the final model. If a confounder was identified, the model
with the lowest Akaike information criterion (AIC) was
Analysis of residual spatial dependence
Residuals from the final multivariable models for each
STH species were extracted for each survey year and
residual spatial dependence was estimated using
semivariograms, constructed using the geoR package of the
statistical software R (The R Foundation for Statistical
]. Semivariograms are defined by three
parameters, namely the nugget, the range and the sill.
The sill is constituted by the sum of the partial sill and
the nugget. The partial sill and nugget correspond,
respectively, to the components of residual variation that
are spatially structured and unstructured variation (e.g.
random error). The range indicates the average size of
clusters of STH prevalence. The proportion of the
variance in the data that is due to geographical location can
be estimated by dividing the partial sill by the sill. A
spatial trend in prevalence of infection is present when
the sill of a semivariograms is not attained within a
reasonable range, indicating the range is very large relative
to the study area. Propensity for clustering is calculated
by the partial sill divided by the sum of the partial sill
and the nugget.
Spatial risk prediction and model validation
A total of 40,656 individual observations of STH
infection status across all years were included in the analysis.
Spatial modelling was conducted on data collected
between 2007 and 2011 and separately for 2014. Spatial
prediction of STH prevalence was performed for each
year using model-based geostatistics [
] with the Bayesian
statistical software, OpenBUGS version 1.4 (Medical
Research Council Biostatistics Unit, Cambridge, UK
and Imperial College London, London, UK). All models
included time, individual and environmental covariates as
fixed effects plus a geostatistical random effect, in which
spatial autocorrelation between locations was modelled
using an exponentially decaying autocorrelation function.
To improve identifiability and model convergence, all
environmental variables were standardised by subtracting
the mean and dividing by the standard deviation. The
resulting regression coefficients for these variables
represent the effect of a change of one standard deviation in
The outputs of Bayesian models, including parameter
estimates and spatial prediction at unsampled locations,
are distributions termed “posterior distributions”. The
posterior distributions represent fully the uncertainties
associated with the parameter estimates. We summarised
the posterior distributions in terms of the posterior mean
and standard deviation. Predicted prevalence estimates
were categorised into 6 categories for visualisation:
category 1 indicates very low STH prevalence (< 2%);
category 2 indicates low prevalence (2–10%); category
3 indicates moderate STH prevalence (10–20%); category
4 indicates moderately-high prevalence (20–50%);
category 5 indicates high prevalence (50–80%; and category 6
very high prevalence (> 80%). Prediction uncertainty was
defined by the standard deviation and was categorised into
3 categories: low uncertainty (standard deviation < 0.2),
moderate uncertainty (standard deviation 0.2–0.5) and
high uncertainty (standard deviation > 0.5). Estimation of
surface areas was conducted in ArcGIS using raster
calculators and zonal statistics.
The predictive accuracy of the prevalence of infection
models was assessed using the mean prediction error,
the mean absolute error and the correlation coefficient
between the predicted and observed values. The mean
error quantifies the bias of the predictor, and the
mean absolute error provides a measure for the
association between the observed and predicted values.
The correlation between the observed and predicted
data was tested using Pearson’s correlation coefficient
(Additional file 1: Table S3).
Estimation of number of school age children at risk of STH
Population density maps were multiplied by the predicted
prevalence maps in ArcGIS version 10.3 (ESRI, Redlands,
CA) to estimate the number of SAC predicted to be
infected with each of the STH species per year per district.
Population data for Burundi were obtained from
CIESIN2000, and population growth rates for years 2005 to
2014 were obtained from the World Bank [
]. To estimate
population for each survey year, the base population figure
from 2011 was multiplied by the population growth rate.
Dataset for analysis
All variables, with the exception of precipitation (for
which a quadratic relationship was explored), had a
linear relationship with STH infection prevalence.
Precipitation was subsequently excluded from the
final multivariable model because it was not
statistically significantly associated with prevalence of
infection. Initial univariate analyses demonstrated that
LST and elevation were highly correlated, with a
Pearson’s correlation coefficient of 0.9. However, the
P-value and AIC scores for LST was lower than the
P-value for elevation and for that reason elevation
was excluded from the multivariable analysis. In the
multivariate models, only LST and NDVI were found
to be associated (P > 0.05) with the prevalence of all
parasites at each survey location.
Residual spatial variation
The residual semivariograms for A. lumbricoides
prevalence of infection indicate that, after accounting for the
effect of environmental covariates, infections were
clustered during the years 2010 (average cluster size: 68 km;
propensity for clustering: 80%) and 2011 (average cluster
size: 77 km; propensity for clustering: 93%) (Additional
file 1: Figure S1a-e). For T. trichiura, residual
geographical clustering was present in 2008 (average cluster size:
52 km; propensity for clustering: 100%) and 2009
(average cluster size: 61 km; propensity for clustering: 100%)
(Additional file 1: Figure S2a-e, Table S4). For hookworm
infections, clustering was only found in 2008 and spatial
trends in 2009 and 2010 (average cluster size: 22 km;
propensity for clustering: 75%) (Additional file 1: Figure
S3a-e, Table S4). In 2014, residual semivariograms for A.
lumbricoides and hookworm prevalence demonstrated
trends in spatial dependence, whilst no spatial
dependence was evident for T. trichiura.
Spatial risk prediction
Model effect sizes for each parasite between 2008 and
2011 and 2014 can be found in Additional file 1: Table
S5. Predictive prevalence maps for both A. lumbricoides
(Fig. 1) and T. trichiura (Fig. 2) demonstrate that the
western region, the eastern border, the south-eastern
border region and the north-eastern region of the
country experienced a gradual reduction in STH prevalence
from 2008 until 2014. Our predictive prevalence maps
for A. lumbricoides show that between 2008 and 2014,
the central south-western and north-western regions of
the country areas demonstrated continued moderately
high prevalence (> 20% and less than 50%) after several
rounds of MDA were observed. Furthermore, areas to
the north-west of the country experienced an increase in
prevalence in 2014. Our predictive prevalence maps for
T. trichiura show that in the central-northern region of
the country there was a slight reduction in prevalence.
This region, however, also maintained higher prevalence
values (> 10% and less than 20%) than the surrounding
regions; this is particularly evident between 2008 and
2011. In 2014, a small region where moderate prevalence
(> 10% and less than 20%) of infection is predicted
appeared in the south-western region of the country
with a prevalence higher than in 2008. Our predictive
prevalence maps for hookworm (Fig. 3) indicate that in
2008 the west and eastern regions had the highest
predicted prevalence of infection (between 20 and 50%); by
2011 these regions observed a significant reduction in
prevalence (predicted prevalence reaching 10–20%).
However, in 2014 prevalence of hookworm infection was
predicted to be as high as 50% in the north southwest
and small pockets in the east of the country.
For all parasite species, there was a substantial
reduction in the total surface area of high and moderate
prevalence categories between 2008 and 2011 with a
resurgence in 2014 (Table 1). For all parasites our results
indicate a decrease in the overall surface area of
moderate and high prevalence categories from 15,734 m2 in
2008 to 4277 m2 in 2011. The results for A. lumbricoides
demonstrate a reduction in the surface areas for high
infection prevalence (> 50% and less than 80%) and
moderate infection prevalence (> 20% and less than 50%)
between 2008 and 2011 and an increase in 2014, with an
overall total surface area of 10,310 km2 in 2008,
4277 km2 in 2011, and 6802 km2 in 2014. These changes
were accompanied by a substantial increase in surface
area of regions within the low infection prevalence
category. For T. trichiura, there was a reduction in surface
area for high (> 50% and less than 80%) and moderately
high infection prevalence (> 20% and less than 50%)
from 416 km2 in 2008 to 0 km2 in 2011 and an increase
of 286 km2 in 2014. For T. trichiura there were no very
high prevalence categories (> 80%) from 2008 to 2014,
with all high prevalence areas (> 50% and less than 80%)
transitioned to a moderately high prevalence (> 20% and
less than 50%) status. Nearly all moderate prevalence
categories (> 10% and less than 20%) transitioned into
low prevalence (> 2% and less than 10%) categories. In
2008, very few areas were classified with very low
prevalence (< 2%) (surface area 346 km2); however, by
2014 most areas in the country were classified with very
low prevalence (surface area 13,006 km2). For hookworm
there was a substantial decline in moderate prevalence
surface area between 2008 and 2011, from 4646 km2 to
0 km2; however, a resurgence of the moderate and high
prevalence categories was evident in 2014, with a total
surface area of 3079 km .
For A. lumbricoides, regions in the north, south and
east of the country showed low to very low prediction
uncertainty. Low (standard deviation below 0.2) to
moderate (standard deviation between 0.2–0.5) uncertainty
was evident in the central and western regions of the
country (Additional file 1: Figure S4). Predictions for T.
trichiura had low to very low uncertainty throughout
the country. Patches of low to moderate uncertainty
were evident in the centre of the country between 2008
and 2011, whilst in 2014 moderate uncertainty
corresponded closely to areas of moderate prevalence of
infection (Additional file 1: Figure S5). For hookworm,
uncertainty was low across the country between 2008
and 2011. However, in 2014, moderate uncertainty was
evident in the northern, eastern and southern regions of
the country (Additional file 1: Figure S6).
The models for A. lumbricoides prevalence
demonstrated low mean absolute error (MAE) for all years
(ranging between 0.03 and 0.06) with high Pearson’s
correlation coefficients (PCC) (ranging between 0.84
and 0.98) for all years (Additional file 1: Table S3). The
models for T. trichiura prevalence demonstrated low
mean absolute error for all years (MAE between 0.01
and 0.04) with high Pearson’s correlation coefficients
(ranging between 0.93 and 0.94) for 2008, 2009 and
2011. Correlation was weak in 2010 and 2014, ranging
between 0.16 and 0.47 (Additional file 1: Table S3).
The models for hookworm prevalence also demonstrated
low mean absolute error (ranging between 0.03 and 0.04)
and high Pearson’s correlation coefficients (ranging between
0.74 and 0.83) for all years (Additional file 1: Table S3).
Spatial heterogeneity in the number of school age children infected with STH
An overall reduction in the number of infected SAC was
evident for all parasite species from 2008 to 2011 in all
districts. In 2014, an estimated total of 4,098,816
children were infected with at least one species, either A.
lumbricoides (Fig. 4), T. trichiura (Fig. 5) or hookworms
(Fig. 6). A reduction in the predicted number of children
infected with A. lumbricoides was evident from 2008 to
2011, with the highest predicted number of infected
children in 2008 being 119,619 infected children in the Gitega
district for A. lumbricoides and in 2014 in the Kibuye
Prevalence of T. trichiura infections
Prevalence of hookworm infections
district, with 128,903 children infected. For T.
trichiura, and in 2008, the district with the highest
number of infected children was Ngozi, with 65,669
infected children. In 2014, the Mabayi district was
predicted to have the highest number of SAC with T.
trichiura, with 35,302 infected children. In 2008,
hookworm infection was highest in the Muyinga
district, with an estimated 66,828 children infected with
N. americanus/A. duodenale. In 2014, this figure
increased to 87,511 in Kiremba. Overall, the number of
children infected with hookworm saw a 4.9% increase
between 2008 and 2014 (Additional file 1: Table S6).
In our previous study we reported marked variation in
STH prevalence between the different years of the
Burundian MDA programme, thus justifying a more
formal assessment of the spatiotemporal distribution of
STH prevalence in Burundi [
]. Here we quantify, for
the first time, the impact of an 8-year MDA
programme on the spatiotemporal variation in
infection prevalence and predict the number of children
infected with each STH species over the course of the
programme. The maps and infection burden estimates
presented here can help intervention planning to best
utilise resources to ensure that areas that are most at
risk of STH infection are targeted [
maps could also be useful to guide the control
programme in Burundi on how to best reach
transmission control and elimination goals by linking with
transmission dynamics models .
The effect of socio-economic, climate and physical
environment on STH infections is well known [
has been used to investigate the spatial distribution of
STH infections [
20, 31, 32
]. In our multivariable models
of prevalence of A. lumbricoides and T. trichiura
infections, we found that areas with higher prevalence of
infection were associated with temperatures between 25
and 37 °C and lower vegetation indexes. This is in line
with existing evidence indicating that A. lumbricoides
and T. trichiura species require temperatures below 37 °C
and low NDVI values to facilitate their survival and
Spatiotemporal variation in STH infection prevalence
Quantifying the relative change in the geographical
clustering in different parasite species over the duration of
successive annual MDAs can help determine the
population effect of anthelmintic treatment and the likelihood
of particular areas achieving elimination. It is expected
that, as a result of MDA, clusters of high prevalence of
infection will be reduced in size as areas less resistant to
treatment shift their level of endemicity to moderate and
low prevalence of infection. As a result of successive
MDA rounds between 2008 and 2010, prevalence of
STH infection was reduced [
]; the results of the present
study demonstrate that the observed reduction in
prevalence between 2009 and 2011 was accompanied by a
concurrent reduction in the geographical clustering of
STH infections, particularly for T. trichuris and
hookworm infections, as evidenced by a reduction in the
propensity for clustering (from 2009 to 2014 in the case of
both species). This finding is corroborated by our
predictive prevalence maps which indicate a reduction in
the prevalence of T. trichiura infection in central
districts of the country and along the periphery of the
country in the case of hookworm where these infections
where principally distributed.
This suggests that during 2008–2011 geographical
patterns of T. trichiura and hookworm infections shifted
from widespread high-endemicity clusters into less
defined prevalence clusters but still exhibited some residual
spatial trend in infection. In the case of A. lumbricoides,
despite the reduction in prevalence of infection, the spatial
patterns from 2008 to 2011 remained relatively stable,
with highly endemic areas present in the central districts.
However, resurgence in prevalence of infection was
detected in 2014 in the northwest and southwest regions
of the country for A. lumbricoides and T. trichiura and
in the northwest, southwest, east and northeast for
hookworm. This increase resulted in the re-emergence
of the moderate infection prevalence category, with A.
lumbricoides also experiencing resurgence in the high
prevalence category. The precise reasons for this
resurgence are largely unknown since the longitudinal study
concluded in 2011 and follow-up surveys were not
conducted until 2014.
Areas of civil unrest were mainly documented in the
western, south-western, north-western and north-eastern
regions of the country [
]. The original 12 pilot
school sites (situated mainly in the west of the country
) remained relatively safe, while in all extension survey
sites (more evenly distributed throughout the country [
treatment was halted in 2010 due to disruption by the civil
unrest. Disruption of the MDA programme in affected
areas is likely to have impacted on the spatial distribution
of infections due to uneven coverage of MDA. Central
and eastern areas received the majority of the
internallydisplaced population and there was a notable division
between treated and untreated populations there. The
12-month treatment disruption and the observed
impacts highlight the potential impact of population
dynamics and contextualising population movement in
the context of STH transmission; the importance of
developing spatially-structured dynamic models in addition to
spatially-structured geostatistical models; and the need to
develop WASH infrastructure that would change
prevailing transmission conditions more sustainably.
The above regions’ higher initial prevalence rates, for
example in Kibumbu, Gitega and their immediate
], could also be a factor in resurgence. Even
currently hypo-endemic areas may have an increased
risk of resurgence or reintroduction if they were
formerly hyper-endemic, thus emphasizing that MDA
programmes alone are not sustainable in maintaining
low morbidity in the long term in areas prone to
destabilisation. Moreover, the overall geographical
distribution of hookworm species appeared to be inversely
associated with that of A. lumbricoides and T. trichiura.
This, together with the fact that the different STH
species are characterised by different age profiles of
infection, highlights the need to understand the macro- and
micro-epidemiology of the STH component infections
Spatial variation in treatment needs following 8-year
MDA in Burundi
Combining infection prevalence maps with estimates of
population numbers has allowed us to: (i) estimate the
temporal variation in the predicted number of infected
SAC over different years of the MDA programme in
Burundi; (ii) identify areas where reductions in these
numbers were more or less pronounced and, therefore,
to highlight areas where the number of infected SAC
remained roughly unaltered; and (iii) predict
geographically the number of SAC infected for 2014, the year
during which a “national reassessment” of the programme
was conducted. By taking population density into
account, our results demonstrate that in the case of A.
lumbricoides and T. trichiura, the central and central
northern regions of Burundi should be the focus of
future MDA programmes, as these contain communities
where the number of infected children is predicted to be
highest. However, in the case of hookworm infection,
the eastern western region as well as the northern
regions should be of particular focus. Predictive infection
distribution maps are an important extension that allow
for effective and programmatically helpful
decisionsupport tools to target treatments to populations in
greatest need. An important extension to our work
could involve coupling our models to dynamic disease
transmission models that account for internal population
A number of limitations need to be considered when
interpreting our results. First, our results indicated that
areas of moderate uncertainty are co-distributed with
areas of moderate to high prevalence. This may be so
because our data had few cases of moderate and heavy
infections from 2009 onwards. One of the principal
purposes of evaluating the level of uncertainty in mapped
outputs is to demonstrate areas where further
investigations are needed [
]. Second, the presence and intensity
of STH infections are determined by poor hygiene and
sanitation, and socio-economic demographics [
3, 36, 37
but data indicating the state of hygiene practices and the
availability of sanitation infrastructure in the study
districts were not available. Third, although we
endeavoured to obtain remotely-sensed data with the highest
possible resolution, in some instances, the resolution of
the data was not ideal (with pixels approximating 1 km
by 1 km). This is a limiting factor as it contributes to
regression dilution bias. Similarly, population maps used
in our models have been adjusted using general annual
growth rates and as such they are subject to accuracy
issues as annual growth rates may have not been
necessarily homogenous across the entire nation. Fourth, we did
not account in our modelling framework for the impact
of other NTD interventions, such as treatment of
onchocerciasis, which not only is community wide (rather
than targeted at particular age and population groups),
but also includes ivermectin, an anthelmintic which,
when combined with ABZ, has a better efficacy for T.
trichiura than ABZ or MBZ on their own [
]. This gap
may act as a critical factor influencing the differences
observed between 2011 and 2014, but it was difficult to
obtain programmatic data for these two programmes and
the extent of their overlap with the STH programme.
Finally, while our validation statistics demonstrate high
correlation and low mean errors for most parasite species and
years, this was not the case for T. trichiura in 2010 and
2014, where Pearson’s correlation coefficient was poor (i.e.
< 0.7). This is likely due to the fact that more than 30% of
the survey locations had no T. trichiura infections for the
target age and sex subpopulation of our prediction model.
Follow-up parasitological surveys, as well as MBG
mapping updates throughout the programme, have been
used to monitor the overall progress achieved with the
STH MDA intervention in Burundi from 2007 to 2014
in terms of changes in the spatiotemporal clustering of
prevalence, surface area of endemicity levels and
numbers of children at risk. Together with a decrease in
prevalence, a decrease in infection clustering was also
observed, suggesting that successive MDA rounds were
successful at reducing infection clusters [
infection patterns from clusters of high to moderate
infection levels to more dispersed cases of infection. This was
evident for all parasite species over the course of the
MDA programme. Furthermore, the small-scale
geographical distribution of STH species also changed over
the course of this programme. The number of infected
SAC varied geographically across the years and for the
different parasite species. Finally, the success of the
MDA programme appears to be very sensitive to
perturbations to the programme and possibly to internal
migration with and areas rebounding to higher prevalence
levels in a matter of a couple of years.
Additional file 1: Table S1. Environmental data summary. Table S2.
Correlation coefficients for NDVI and LST, 2007–2011. Table S3. Model
validation results, mean prediction error, absolute prediction error and
Pearson’s correlation coefficient for all parasites, 2008–2011 and 2014.
Table S4. Average size of clusters and propensity of clustering per
parasite per year. Table S5. Model effect sizes for all parasites, 2008–2011
and 2014. Table S6. Total number of infected children per year per
parasite 2008–2011 and 2014. Figure S1. Residual semivariograms for
prevalence of infection with Ascaris lumbricoides in Burundi for years
2007–2011 and 2014. Figure S2. Residual semivariograms for prevalence
of infection with Trichuris trichiura in Burundi for years 2007–2011 and
2014. Figure S3. Residual semivariograms for prevalence of infection
with hookworm in Burundi for years 2007–2011 and 2014. Figure S4.
Posterior mean standard deviation of predicted prevalence of infection
with Ascaris lumbricoides in Burundi for 2008–2011 and 2014. Figure S5.
Posterior mean standard deviation of predicted prevalence of infection
with Trichuris trichiura in Burundi for 2008–2011 and 2014. Figure S6.
Posterior mean standard deviation of predicted prevalence of infection
with hookworm in Burundi for 2008–2011 and 2014. (DOCX 761 kb)
95% CI: 95% confidence interval; ABZ: Albendazole; AIC: Akaike information
criterion; ASTER: Advanced spaceborne thermal emission and reflection
radiometer; AUC: Area under curve; CIESIN: Center for International Earth
Science Information Network; DEM: Digital elevation model; DPWB: Distance
to perennial water body; GDEM: Global digital elevation map; GEE: Google
Earth Engine; GIS: Geographical information systems; GLM: Generalised linear
models; GPS: Global positioning system; GRUMP: Global rural urban mapping
project; LST: Land surface temperature; MAE: Mean absolute error; MBG:
Modelbased geostatistics; MBZ: Mebendazole; MDA: Mass drug administration;
NDVI: Normalized differential vegetation index; NTD: Neglected tropical
diseases; PCC: Pearson’s correlation coefficient; ROC: Receiver operating
characteristic; SAC: School-aged children; SCI: The Schistosomiasis Control
Initiative; SCORE: Schistosomiasis Consortium for Operational Research and
Evaluation; SSA: Sub-Saharan Africa; STH: Soil-transmitted helminths;
WASH: Water and sanitation and hygiene; WHO: World Health Organization
We thank all the partners involved in this programme (the Ministry of Health
in Burundi, Schistosomiasis Control Initiative, CBM, the Global Network
Neglected Tropical Disease Control, and Geneva Global), school directors,
laboratory technicians and nurses that have made this work possible.
The reported monitoring and evaluation programme was financially supported
by Geneva Global and the Global Network for Tropical Diseases (GNTD) in
collaboration with the Sabine Institute, and by the Schistosomiasis Control
Initiative, which provided also technical assistance. The reported national
mapping survey conducted in 2014 was funded by the Schistosomiasis
Consortium for Operational Research and Evaluation (SCORE) and
Schistosomiasis Control Initiative.
Availability of data and materials
According to institutional agreements with the Minister of Health in Burundi,
full dataset can be shared upon the Ministry of Health’s permission. Therefore,
the full dataset cannot be shared at this stage.
MA, RJSM and GO conceived and designed the study. GO and team
executed the study and coordinated the field work. GO supervised the
data collection. MA and RJSM collated the data from all studies, cleaned
it and started the data analysis. MA analysed the data. RJSM supervised
the data analysis. MA wrote the first draft of the manuscript. GO, RJSM,
MGB, CL, KH, AC and AF reviewed the manuscript critically and
contributed intellectual input. MA prepared the final draft. All authors
read and approved the final manuscript.
Mohamad Assoum: A PhD candidate at the University of Queensland,
Australia focusing on the spatial epidemiology of STH infections in child
populations. Background in medical geography and international public
health (BSc, GradDip GIS, MIPH(hon)). Working in collaboration with SCI at
Imperial College London. Giuseppina Ortu: Senior Programme Manager at
Schistosomiasis Control Initiative (Imperial College, London, UK) at the time
of the programme in Burundi, and currently Senior NTD specialist at Malaria
Consortium (London, UK) and Honorary Research Associate at Imperial College
(London, UK). Colleen Lau: Infectious disease epidemiologist and clinician, and
currently an NHMRC Fellow at the Department of Global Health, Research School
of Population Health, The Australian National University. Kate Halton: Lecturer in
Health Economics, School of Public Health & Social Work, Queensland University
of Technology. Senior Research Fellow in Infectious Disease, Australian Centre for
Health Services Innovation. María-Gloria Basáñez: Professor of Neglected Tropical
Diseases at Imperial College London; member of the London Centre for
Neglected Tropical Disease Research and long-standing collaborator of the
Schistosomiasis Control Initiative. Alan Fenwick: Director, Schistosomiasis
Control Initiative in the Department of Infectious Disease Epidemiology,
Imperial College London. Archie Clements: Director, Research School of
Population Health. An infectious disease epidemiologist working on
neglected tropical diseases and other tropical infections. Ricardo J Soares
Magalhaes: Senior Lecturer in Population Health and Veterinary Biosecurity, School
of Veterinary Science, Faculty of Science, The University of Queensland. Affiliate
Senior Lecturer UQ Children’s Health Research Centre.
Ethics approval and consent to participate
Ethical clearance for all surveys was obtained from the Ministry of Health
(MoH) in Burundi. Written and verbal consent were obtained from the MoH,
school teachers and the parents or guardians of the children recruited in the
study. Ethical clearance was also obtained by the SCI from Imperial College
Research Ethics Committee (ICREC_8_2_2). Data were anonymized prior to
analysis by assigning each participant a unique identification number. For all
surveys, children found positive for STH infection at the time of surveys
received treatment immediately. In addition, treatments were administered
to all children in endemic schools during the MDA rounds, which occurred
between two and four weeks after surveys were conducted.
Consent for publication
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
1. Pullan RL , Smith JL , Jasrasaria R , Brooker SJ . Global numbers of infection and disease burden of soil-transmitted helminth infections in 2010 . Parasit Vectors. 2014 ; 7 ( 1 ): 37 .
2. Neglected Tropical Diseases , Uniting to Combat: london declaration on neglected tropical diseases . 2012 . http://unitingtocombatntds.org/sites/ default/files/document/london_declaration_on_ntds. pdf. Accessed 20 Mar 2016 .
3. Campbell SJ , Savage GB , Gray DJ , Atkinson J-AM , Soares Magalhães RJ , Nery SV , et al. Water, sanitation, and hygiene (WASH): a critical component for sustainable soil-transmitted helminth and schistosomiasis control . PLoS Negl Trop Dis . 2014 ; 8 : e2651 .
4. Truscott JE , Turner HC , Farrell SH , Anderson RM . Soil-transmitted helminths: mathematical models of transmission, the impact of mass drug administration and transmission elimination criteria . Adv Parasitol . 2016 ; 94 : 133 - 98 .
5. Ndayishimiye O , Ortu G , Soares Magalhaes RJ , Clements A , Willems J , Whitton J , et al. Control of neglected tropical diseases in Burundi: partnerships, achievements, challenges, and lessons learned after four years of programme implementation . PLoS Negl Trop Dis . 2014 ; 8 : e2684 .
6. Ortu G , Assoum M , Wittmann U , Knowles S , Clements M , Ndayishimiye O , et al. The impact of an 8-year mass drug administration programme on prevalence, intensity and co-infections of soil-transmitted helminthiases in Burundi . Parasit Vectors . 2016 ; 9 : 513 .
7. Freeman MC , Clasen T , Brooker SJ , Akoko DO , Rheingans R. The impact of a school-based hygiene, water quality and sanitation intervention on soiltransmitted helminth reinfection: a cluster-randomized trial . Am J Trop Med Hyg . 2013 ; 89 : 875 - 83 .
8. Mascarini-Serra L . Prevention of soil-transmitted helminth infection . J Glob Infect Dis . 2011 ; 3 : 175 - 82 .
9. Minamoto K , Mascie-Taylor CGN , Karim E , Moji K , Rahman M . Short- and long-term impact of health education in improving water supply, sanitation and knowledge about intestinal helminths in rural Bangladesh . Public Health . 2012 ; 126 ( 5 ): 437 .
10. Pullan RL , Gething PW , Smith JL , Mwandawiro CS , Sturrock HJ , Gitonga CW , et al. Spatial modelling of soil-transmitted helminth infections in Kenya: a disease control planning tool . PLoS Negl Trop Dis . 2011 ; 5 : e958 .
11. Magalhães RJS , Biritwum N-K , Gyapong JO , Brooker S , Zhang Y , Blair L , et al. Mapping helminth co-infection and co-intensity: geostatistical prediction in Ghana . PLoS Negl Trop Dis . 2011 ; 5 : e1200 .
12. Clements ACA , Garba A , Sacko M , Toure S , Dembele R , Landoure A , et al. Mapping the probability of schistosomiasis and associated uncertainty . West Africa Emerg Infect Dis . 2008 ; 14 : 1629 .
13. Clements ACA , Lwambo NJS , Blair L , Nyandindi U , Kaatano G , Kinunghi S , et al. Bayesian spatial analysis and disease mapping: tools to enhance planning and implementation of a schistosomiasis control programme in Tanzania . Tropical Med Int Health . 2006 ; 11 : 490 - 503 .
14. RJS M , ACA C , Patil AP , Gething PW , Brooker S. The applications of modelbased geostatistics in helminth epidemiology and control , vol. 74 . England: Elsevier Science & Technology; 2011 . p. 267 - 96 .
15. Clements A , Deville M , Ndayishimiye O , Brooker S , Fenwick A . Spatial codistribution of neglected tropical diseases in the east African great lakes region: revisiting the justification for integrated control . Tropical Med Int Health . 2010 ; 15 : 198 - 207 .
16. Assefa LM , Crellen T , Kepha S , Kihara JH , Njenga SM , Pullan RL , et al. Diagnostic accuracy and cost-effectiveness of alternative methods for detection of soil-transmitted helminths in a post-treatment setting in western Kenya . PLoS Negl Trop Dis . 2014 ; 8 : e2843 .
17. Knopp S , Speich B , Hattendorf J , Rinaldi L , Mohammed KA , Khamis IS , et al. Diagnostic accuracy of Kato-Katz and FLOTAC for assessing anthelmintic drug efficacy (diagnostics and anthelmintic drug efficacy) . PLoS Negl Trop Dis . 2011 ; 5 : e1036 .
18. Speich B , Knopp S , Mohammed KA , Khamis IS , Rinaldi L , Cringoli G , et al. Comparative cost assessment of the Kato-Katz and FLOTAC techniques for soil-transmitted helminth diagnosis in epidemiological surveys . Parasit Vectors . 2010 ; 3 : 71 .
19. Brooker S , Clements ACA , Bundy DAP . Global epidemiology, ecology and control of soil-transmitted helminth infections , vol. 62 . England: Elsevier Science & Technology; 2006 . p. 221 - 61 .
20. Brooker S , Clements ACA , Bundy DAP . Global epidemiology, ecology and control of soil-transmitted helminth infections . Adv Parasitol . 2006 ; 62 : 221 - 61 .
21. Nations FaAOotU. http://www.fao.org/geonetwork/srv/en/main.home.
22. University EIaC. http://sedac.ciesin.columbia.edu/gpw/global.jsp.
23. Pullan RL , Sturrock HJ , Soares Magalhaes RJ , Clements AC , Brooker SJ . Spatial parasite ecology and epidemiology: a review of methods and applications . Parasitology . 2012 ; 139 : 1870 - 87 .
24. Magalhães RJS , Barnett AG , Clements ACA . Geographical analysis of the role of water supply and sanitation in the risk of helminth infections of children in West Africa . Proc Natl Acad Sci USA . 2011 ; 108 : 20084 - 9 .
25. The World Bank. http://databank.worldbank.org/data/reports.aspx?Code=NY. GDP.PCAP.CD&id=af3ce82b&report_name=Popular_indicators&populartype= series&ispopular=y. Accessed 20 Mar 2016 .
26. Brooker S , Kabatereine NB , Gyapong JO , Stothard JR , Utzinger J . Rapid mapping of schistosomiasis and other neglected tropical diseases in the context of integrated control programmes in Africa . Parasitology. 2009 ; 136 : 1707 - 18 .
27. Kabatereine NB , Standley CJ , Sousa-Figueiredo JC , Fleming FM , Stothard JR , Talisuna A , et al. Integrated prevalence mapping of schistosomiasis, soiltransmitted helminthiasis and malaria in lakeside and island communities in Lake Victoria, Uganda . Parasit Vectors . 2011 ; 4 : 232 .
28. Standley CJ , Adriko M , Alinaitwe M , Kazibwe F , Kabatereine NB , Stothard JR . Intestinal schistosomiasis and soil-transmitted helminthiasis in Ugandan schoolchildren: a rapid mapping assessment . Geospat Health . 2009 ; 4 : 39 - 53 .
29. Basáñez M-G , McCarthy JS , French MD , Yang G-J , Walker M , Gambhir M , et al. A research agenda for helminth diseases of humans: modelling for control and elimination . PLoS Negl Trop Dis . 2012 ; 6 : e1548 .
30. Chammartin F , Scholte RGC , Guimarães LH , Tanner M , Utzinger J , Vounatsou P . Soil-transmitted helminth infection in South America: a systematic review and geostatistical meta-analysis . Lancet Infect Dis . 2013 ; 13 : 507 .
31. Pullan RL , Bethony JM , Geiger SM , Cundill B , Correa-Oliveira R , Quinnell RJ , et al. Human helminth co-infection: analysis of spatial patterns and risk factors in a Brazilian community . PLoS Negl Trop Dis 2008 ; 2 : e352 .
32. Scholte RGC , Schur N , Bavia ME , Carvalho EM , Chammartin F , Utzinger J , et al. Spatial analysis and risk mapping of soil-transmitted helminth infections in Brazil, using Bayesian geostatistical models . Geospat Health . 2013 ; 8 : 97 - 110 .
33. Hoofnagle K , Rothe D . Overlooked and overshadowed: the case of Burundi . Crit Crim . 2010 ; 18 : 169 - 89 .
34. Schneider J . Lessons from Burundi's unrest . New African . 2015 ; 551 : 36 - 8 .
35. Araujo Navas AL , Hamm NAS , Soares Magalhães RJ , Stein A . Mapping soiltransmitted helminths and schistosomiasis under uncertainty: a systematic review and critical appraisal of evidence . PLoS Negl Trop Dis . 2016 ; 10 : e0005208 .
36. Asaolu SO , Ofoezie IE . The role of health education and sanitation in the control of helminth infections . Acta Trop . 2003 ; 86 : 283 - 94 .
37. Blair P , Diemert D. Update on prevention and treatment of intestinal helminth infections . Curr Infect Dis Rep . 2015 ; 17 : 1 .
38. Cairncross S , Blumenthal U , Kolsky P , Moraes L , Tayeh A . The public and domestic domains in the transmission of disease . Tropical Med Int Health . 1996 ; 1 : 27 - 34 .