Spatiallyexplicit risk profiling of Plasmodium falciparum infections at a small scale: a geostatistical modelling approach
 Kigbafori D Silué†^{1, 2},
 Giovanna Raso†^{3, 4}Email author,
 Ahoua Yapi^{1},
 Penelope Vounatsou^{5},
 Marcel Tanner^{5},
 Eliézer K N'Goran^{1, 2} and
 Jürg Utzinger^{5}
DOI: 10.1186/147528757111
© Silué et al; licensee BioMed Central Ltd. 2008
Received: 14 March 2008
Accepted: 23 June 2008
Published: 23 June 2008
Abstract
Background
There is a renewed political will and financial support to eradicate malaria. Spatiallyexplicit risk profiling will play an important role in this endeavour. Patterns of Plasmodium falciparum infection prevalence were examined among schoolchildren in a highly malariaendemic area.
Methods
A questionnaire was administered and finger prick blood samples collected from 3,962 children, aged six to 16 years, attending 55 schools in a rural part of western Côte d'Ivoire. Information was gathered from the questionnaire on children's socioeconomic status and the use of bed nets for the prevention of malaria. Blood samples were processed with standardized, qualitycontrolled methods for diagnosis of Plasmodium spp. infections. Environmental data were obtained from satellite images and digitized maps. Bayesian variogram models for spatiallyexplicit risk modelling of P. falciparum infection prevalence were employed, assuming for stationary and nonstationary spatial processes.
Findings
The overall prevalence of P. falciparum infection was 64.9%, ranging between 34.0% and 91.9% at the unit of the school. Risk factors for a P. falciparum infection included age, socioeconomic status, not sleeping under a bed net, distance to health care facilities and a number of environmental features (i.e. normalized difference vegetation index, rainfall and distance to rivers). After taking into account spatial correlation only age remained significant. Nonstationary models performed better than stationary models.
Conclusion
Spatial risk profiling of P. falciparum prevalence data provides a useful tool for targeting malaria control intervention, and hence will play a role in the quest of local elimination and ultimate eradication of the disease.
Background
Malaria remains a leading cause of morbidity and mortality in tropical and subtropical regions of the world. There are an estimated three billion people at risk of this disease and more than half a billion episodes of clinical Plasmodium falciparum occur each year, killing over one million individuals annually [1, 2]. It has been estimated that the global burden of malaria exceeds 40 million disabilityadjusted life years (DALYs) [2, 3] and the disease drains the social and economic development of affected regions [4, 5]. Highrisk groups are children under the age of five years and pregnant women, with subSaharan Africa particularly affected. Indeed, this part of the world accounts for a striking 90% of the global burden of malaria [6, 7].
Predicting the abundance and spread of malaria in endemic settings, in order to develop locallyadopted malaria control strategies to lower the burden of the disease is a pressing public health issue. Recently, an audacious goal has been announced in Seattle, USA during a meeting led by the Bill and Melinda Gates Foundation, namely to eradicate malaria [8]. This goal – so the claim – has become a realistic hope thanks to new scientific advances, including the development of novel antimalarial drugs, vaccines and integrated control efforts through insecticidetreated nets (ITNs), prophylactic treatment and indoor residual spraying (IRS), in the face of a growing political will and financial support for malaria control initiatives [8]. A deeper understanding of the spatial distribution of malaria is pivotal so that appropriate local elimination efforts can be designed and rigorous monitoring implemented.
Advances made with geographical information system (GIS), remote sensing and geostatistical modelling to predict the spatial and temporal distribution of malaria and Anopheles vectors have opened new avenues in this field of research. In particular, modelling disease and diseaserelated data within a Bayesian framework allows fitting of complex models in quite a flexible way. Additionally, Bayesian approaches provide computational advantages over traditional frequentist approaches via implementation of Markov chain Monte Carlo (MCMC) simulation [9–11]. Recent studies made use of the advantages offered by Bayesian methods for spatiallyexplicit modelling of malaria [12–18].
In Côte d'Ivoire, malaria is one of the primary public health concerns. This is illustrated by a study carried out in the savannah zone that documented malaria being responsible for at least 60% of the consultations in hospitals and 46% in paediatric clinics [19]. In 2005, Côte d'Ivoire ranked at position 13 among countries with the highest rates of underfive mortality and estimates at the time suggested that only 4% of children under five years of age slept under an ITN [20]. In the present study, smallscale patterns and spatial risk factors of the prevalence of P. falciparum among schoolchildren in a rural part of western Côte d'Ivoire were explored, using Bayesian geostatistical models.
Methods
Study area, population and ethical clearance
The study area is the region of Man, located in western Côte d'Ivoire. It is a mountainous region with tropical climate, including rains during eight to nine months of the year, and a dry period between November and February. The landscape is characterized in the north by rounded mountains with altitudes ranging from 200 to 1,300 m above sea level and small valleys, whereas the southern part is a riverdraining plain [21]. The field work was carried out between October 2001 and February 2002.
The study protocol was approved by the institutional research commission of the Swiss Tropical Institute (Basel, Switzerland) and the Centre Suisse de Recherches Scientifiques (Abidjan, Côte d'Ivoire). The study was given ethical clearance from the Ministry of Health in Côte d'Ivoire. All children attending grades three to five from 57 schools in the rural parts of the study area were invited to participate.
School surveys
The education officers were contacted and the aims and procedures of the study were explained. After receipt of their approval, the education officers informed teachers who provided the research team with copies of the class lists, which included information of the children's name, sex and age. First, a questionnaire was administered and schoolchildren were interviewed for assets on ownership and household characteristics, and perceived symptoms and diseases with a recall period of one month. The questionnaire included 17 morbidity indicators (e.g. abdominal pain, fever, etc.) and 12 household assets (e.g. radio, TV, etc.). An assetbased approach was used to stratify schoolchildren into five socioeconomic groups [22, 23]. An additional question was included to inquire whether children slept under a bed net.
Second, a crosssectional survey was carried out, to collect finger prick blood samples from previously interviewed children. Two drops of blood were placed on a microscope slide and thin and thick blood films were prepared. Slides were airdried, transferred to a laboratory in the town of Man and stained with Giemsa. The slides were then forwarded to a reference laboratory in Abidjan and analysed by experienced laboratory technicians for speciesspecific density of Plasmodium, assuming a standard white blood cell (WBC) count of 8,000 per μl of blood by light microscopy. A random sample of 10% of the slides were reexamined by the senior microscopist for quality control purposes. Since more than 95% of the cases were P. falciparum infections, subsequent spatial analyses was restricted on this malaria parasite.
Environmental data
Geographical coordinates of each school were collected using a handheld Magellan 320 global positioning system (GPS; Thales Navigation; Santa Clara, CA, USA). Streets and rivers were digitized with the aid of readily available ground maps. Normalized difference vegetation index (NDVI) and land surface temperature (LST) were downloaded at 1 × 1 km spatial resolution from Moderate Resolution Imaging Spectroradiometer (MODIS) from the USGS EROS Data Centre. Rainfall estimate (RFE) data with an 8 × 8 km spatial resolution from Meteosat 7 satellite were obtained from the Africa Data Dissemination Service (ADDS). NDVI, LST and RFE were downloaded for the period of September, 2001 to August, 2002 and processed as detailed elsewhere [21]. Distances from schools to the nearest healthcare facility and rivers were calculated.
Data management and statistical analysis
Data were entered twice and validated with EpiInfo version 6.4 (Centers for Disease Control and Prevention; Atlanta, GA, USA). Geographical data were displayed in ArcView GIS version 3.2 (Environmental Systems Research Institute, Inc.; Redlands, CA, USA). Schoolchildren were subdivided into two age groups; (i) six to 10 years, and (ii) 11 to 16 years.
All covariates were fitted into bivariate logistic regression models on the P. falciparum infection status variable using STATA version 9.2 (Stata Corporation; College Station, TX, USA). Covariates with a significance level <0.15 were built into (i) a stationary, and (ii) a nonstationary Bayesian logistic regression model for P. falciparum infection, using WinBUGS version 1.4 (Imperial College & Medical Research Council; London, UK). The stationary geostatistical model assumed that spatial correlation is a function of distance only, whereas the nonstationary geostatistical model assumed that spatial correlation is a function of the distance and location [16, 24]. Spatial heterogeneity was taken into account by introducing locationspecific random effects, which model a latent spatial process.
Model specification
Let Y_{ ij }be the P. falciparum infection status of schoolchild j in school i. It is assumed that Y_{ ij }arises from a Bernoulli distribution, Y_{ ij }~Be(P_{ ij }), with probability P_{ ij }. The covariates X_{ ij }and schoolspecific random effect φ_{ i }were modelled on the $\mathrm{log}it({p}_{ij})={\underset{\xaf}{X}}_{ij}^{T}\underset{\xaf}{\beta}+{\phi}_{i}$, that is log it (P_{ ij }), where β is the vector of regression coefficients.
The spatial correlation was introduced on the φ_{ i }'s by assuming that φ = (φ_{1}, φ_{1}, ... φ_{ N })^{ T }has a multivariate normal distribution, φ ~MVN(0, Σ), with variancecovariance matrix Σ. An isotropic spatial process, i.e. Σ_{ mn }= σ^{2} exp (ud_{ mn }), was also assumed, where d_{ mn }is the Euclidean distance between schools m and n, σ^{2} is the geographic variability known as the sill, and u is a smoothing parameter that controls the rate of correlation decay with increasing distance. To take into account nonstationarity, the study area was partitioned in K subregions, assuming a locally stationary spatial process ω_{ k }in each subregion k = 1, ..., K, where ω_{ k }= (ω_{k 1}, ω_{k 2}, ..., ω_{ kN })^{T}. In order to separate the schools into approximately equal numbers, the study area was subdivided into two subregions on a diagonal from the northwestern corner to the southeastern corner. Spatial correlation in the study area was then viewed as a mixture of the different spatial processes and the spatial random effect φ_{ i }at location i was modelled as a weighted average of the subregionspecific (independent) stationary processes as follows: ${\phi}_{i}={\displaystyle \sum _{k=1}^{K}{a}_{ik}{\omega}_{ki}}$, with weights a_{ ik }, which are decreasing functions of the distance between location i and the centroids of the subregions k [25]. Assuming ω_{ k }~ MVN(0, Σ_{ k }) and ${({\sum}_{k})}_{ij}={\sigma}_{k}^{2}corr({d}_{ij};{u}_{k})$, one has $\underset{\xaf}{\phi}=N(\underset{\xaf}{0},{\displaystyle \sum _{k=1}^{K}{A}_{k}^{T}{\sum}_{k}{A}_{k})}$, where A_{ k }= diag{a_{1k}, a_{2k}, ..., a_{ nk }}. The range is defined as the minimum distance at which spatial correlation between locations is below 5%. For an exponential correlation function, it can be calculated as $\raisebox{1ex}{$3$}\!\left/ \!\raisebox{1ex}{${u}_{k}$}\right.$ meters.
Following a Bayesian model specification, prior distributions were adopted for the model parameters. Vague Normal distributions for the β parameters with large variances (i.e. 10,000), inverse gamma priors for ${\sigma}_{k}^{2}$ and uniform priors for u_{ k }, k = 1, ..., K were chosen. MCMC simulation was employed to estimate the model parameters [26]. A single chain sampler with a burnin of 5,000 iterations was run. Convergence was assessed by inspection of ergodic averages of selected model parameters.
Model performance
The deviance information criterion (DIC) was utilized to assess the model performance [27]. For appraisal of the predictive ability of models, a training sample from the current database was used. From the 55 schools, 43 schools (78%) were randomly selected and fitted into the logistic regression models. The remaining 12 schools were utilized for validation purposes. 95%, 75%, 50%, 25% and 1–5% Bayesian credible intervals (BCIs) of the posterior predictive distribution of test locations were calculated. The model with the highest percentage of locations within the BCI with the smallest coverage was considered the best performing one.
Results
Study profile and operational results
Plasmodium infections
Risk profiling and spatial patterns
Results of the bivariate logistic regression model for P. falciparum infections among 3,962 children from 55 rural schools in the Man region, western Côte d'Ivoire.
Indicators  P. falciparum infection prevalence  

OR^{a}  95% CI  Pvalue (AIC^{b})  
Age (years)  
6–10  1  
11–16  0.70  0.61, 0.8  <0.001 
Socioeconomic status  
Most poor  1  
Very poor  1.04  0.84, 1.28  
Poor  1.24  1.01, 1.53  
Less poor  0.95  0.77, 1.16  
Least poor  0.86  0.70, 1.06  0.011 
Sleeping under a bed net  0.79  0.65, 0.98  0.030 
Distance to health care facility  1.17  1.09, 1.25  <0.001 
NDVI  
Mean I^{c}  0.96  0.91, 1.03  0.333 
Mean II^{d}  1.05  0.99, 1.12  0.131 (5139) 
Mean III^{e}  0.96  0.90, 1.03  0.228 
Annual mean  1.20  1.12, 1.29  <0.001 (5110) 
Mean of the transmission season  1.19  1.11, 1.27  <0.001 (5114) 
RFE  
Mean I^{c}  0.88  0.82, 0.94  <0.001 (5126) 
Mean II^{d}  1.04  0.97, 1.11  0.289 
Mean III^{e}  1.13  1.05, 1.20  <0.001 (5129) 
Sum of annual rainfall  1.11  1.04, 1.18  0.003 (5132) 
Mean of the transmission season^{f}  1.14  1.07, 1.21  <0.001 (5125) 
Maximum LST  1.03  0.96, 1.09  0.449 
Distance to rivers (categorized)  
<500 m  1  
500–999 m  1.22  1.03, 1.46  
= 1000 m  0.63  0.54, 0.74  <0.001 
Spatial analyses
Multivariate stationary and nonstationary spatial analyses results for P. falciparum infection prevalence for the Man region, western Côte d'Ivoire.
Indicator  Bayesian logistic regression models  

Stationary  Nonstationary  
OR^{a}  95% BCI^{b}  OR^{a}  95% BCI^{b}  
Age (years)  
6–10  1  1  
11–16  0.75  0.65, 0.87  0.75  0.65, 0.87 
Socioeconomic status  
Most poor  1  1  
Very poor  0.90  0.71, 1.13  0.90  0.71, 1.13 
Poor  1.21  0.95, 1.51  1.21  0.95, 1.51 
Less poor  0.91  0.90, 1.15  0.90  0.71, 1.14 
Least poor  0.85  0.66, 1.08  0.84  0.65, 1.08 
Sleeping under a bed net  0.92  0.72, 1.15  0.92  0.73, 1.15 
Distance to health care facility  1.07  0.87, 1.29  1.04  0.82, 1.27 
Annual mean NDVI  1.16  0.98, 1.38  1.17  0.98, 1.40 
Mean RFE during transmission season  1.06  0.87, 1.27  1.06  0.87, 1.27 
Distance to rivers  
<500 m  1  1  
500–999 m  1.32  0.87, 1.94  1.27  0.81, 1.89 
= 1000 m  0.75  0.48, 1.14  0.72  0.47, 1.09 
ρ _{1} ^{c}  0.0014  0.0003, 0.002  0.0015  0.0003, 0.002 
ρ _{2}  0.0014  0.0004, 0.002  
σ _{1} ^{2d}  0.30  0.17, 0.49  0.23  0.10, 0.48 
σ _{2} ^{2}  0.40  0.18, 0.79  
DIC ^{ e }  4899.8  4900.1 
Model performance
Percentage of test locations with P. falciparum prevalence falling within selected BCIs. For the model validation 43 locations were used for model fitting and 12 for prediction.
BCI  Bayesian logistic regression model  

Stationary  Nonstationary  
95%  93%  100% 
75%  80%  87% 
50%  53%  60% 
25%  27%  27% 
5%  13%  27% 
4%  7%  27% 
3%  7%  27% 
2%  7%  20% 
1%  7%  13% 
Discussion
The purpose of this study was to assess risk factors and smallscale spatial patterns of P. falciparum infection prevalence among schoolchildren in a highly endemic area of rural western Côte d'Ivoire. The following covariates were significantly associated with infection: age, socioeconomic status, sleeping under a bed net, distance to health care facilities and a number of environmental factors. However, after accounting for spatial correlation, only age remained a significant risk factor for P. falciparum prevalence, whereas NDVI showed only 'borderline' significance. The predictive ability of the spatial models was examined using a training sample of 78% of the schools, with the nonstationary model performing better than the stationary one.
There are a number of shortcomings worth discussing. First, only a single finger prick blood sample was collected from each child for microscopic examination. Hence it is conceivable that some infections, particularly those with a low parasitaemia, were missed [28, 29]. Second, it should be noted that schoolaged children in highly malariaendemic areas are not at highest risk of diseaseassociated morbidity and mortality. The prevalence in children below the age of five years might have been even higher than the observed P. falciparum prevalence of 64.9% among six to 16yearold children. Third, the parasitological survey was carried out over a period of several months due to the large number of schoolchildren subjected to interviews and finger prick blood sampling, which might have introduced a bias in the observed prevalence from one school to another due to seasonality. Fourth, in the absence of highresolution data to compute distances to small standing water bodies that might serve as Anopheles breeding sites, information from digitized maps was used to obtain the distance to rivers as an indication for the distance to breeding sites. The most likely vector in this area is Anopheles gambiae and, to some extent, Anopheles funestus. The former vector species breeds in transient, sunlit and generally small pools, whereas the latter has been associated with larger, semipermanent bodies of water containing aquatic vegetation and algae [30].
The analysis presented here showed that schoolchildren from wealthier households were more likely to be infected with P. falciparum compared to schoolchildren from the poorest households. This result is surprising given that the common expectation would be that the poorest of the poor are at highest risk of malaria [31]. Several studies have shown that the burden of malaria is elevated among the poorest population segments, probably because they are at a higher exposure to malaria vectors and have fewer means for personal protective measures. For example, a study carried out in a rural community in Cameroon found a significant relationship between malaria and low protective housing conditions, such as living in wooden plank houses [32]. Surprisingly, no significant association between the risk of a P. falciparum infection and housing conditions was evident in the present study. It is conjectured that issues related to exposure were associated to socioeconomic status, which calls for further investigation. Previous research conducted in rural Tanzania, for example, found that lack of access to health care and preventive measures, including ITNs, was associated with people's socioeconomic status [31]. Interestingly, the current study confirms that children from poorer households were less likely to sleep under a bed net. Furthermore, children who reported sleeping under a bed net were at a decreased risk of having a P. falciparum infection. Additionally, it was found that the risk of a P. falciparum infection was associated with distances to health care facilities. Nevertheless, after taking into account spatial correlation, the covariates socioeconomic status, distance to the nearest health care facility and sleeping under a bed net showed no significant association anymore, and hence other factor must explain the observed spatial heterogeneity of P. falciparum.
Several environmental factors, namely NDVI, RFE and distance to rivers, were significantly associated with a P. falciparum infection in the bivariate nonspatial models. These findings are in accordance with previous studies that showed significant associations between malaria and NDVI, rainfall and distance to rivers at a broader spatial scale [33–35]. It is conceivable that these environmental factors are related to the presence and abundance of malaria vectors, which is governed by suitable breeding and resting sites of Anopheles. An interesting observation in the present study was that children from schools that were located in close proximity to rivers (<500 m) were at a lower risk of a P. falciparum infection compared to more distant schools (between 500 m and 1000 m). Children from schools with distances <500 m were significantly more often reporting to sleep under a bed net, suggesting that the former observation might be partly confounded by a higher level of bed net coverage and usage due to nuisance from mosquitoes near rivers. Children enrolled in schools located at distances >1000 m of rivers were less likely to be infected with P. falciparum, which might be related to the flight range of mosquitoes, which is, on average, below 1 km [36]. Interestingly, none of the environmental covariates showed a statistical significant association to P. falciparum prevalence after accounting for spatial correlation. Hence, the current results demonstrate the importance of accounting for spatial correlation when analysing malaria prevalence data at small spatial scales as reported here. Indeed, omission of spatial correlation would have underestimated the standard errors of the covariate coefficients [37]. Furthermore, in contrast to previous work focussing on helminth infections in the same study area [11, 21, 24, 38], no risk map and corresponding uncertainty map have been presented, since none of the environmental factors investigated was significant in the spatiallyexplicit model. The results therefore suggest that at small spatial scales, individuallevel factors (e.g. age) determine the spatial distribution of the P. falciparum infections rather than coarser environmental factors. These observations suggest that environmental factors are particularly salient for malaria prediction at larger spatial scales.
In geostatistical modelling, the standard assumption is that there is a stationary spatial dependence in the data, which implies that the spatial correlation is a function of the distance between points and independent of the location. Bayesian nonstationary geostatistical models were employed before for the prediction of helminth infections in the same study area [24, 38]. Gosoniu and colleagues were the first to use Bayesian nonstationary geostatistical models for malaria risk, in their recent research on Mali [16] and West Africa [39]. The authors' underlying assumption was that local characteristics related to human behaviour and environment, including vector ecology, influenced spatial correlation differently at different locations over large areas, i.e. an entire country. The results presented here suggest that the use of nonstationary models may also be required at a smaller spatial scale (i.e. at the district level), since the nonstationary model performed better than the one assuming stationarity. The current work on P. falciparum can be integrated with our previous work on helminth infections for mapping P. falciparumhelminth coinfections using multinomial regression models for the simultaneous targeting of malaria and helminthic diseases [11]. Schoolaged children are at the highest risk of such coinfections and data suggest that coinfections with P. falciparum and hookworm have an additive impact on anaemia, implying that those highrisk groups would greatly benefit from integrated malaria and helminth control [40].
Conclusion
An integrated approach that employs different data sources, GIS and remote sensing technologies and Bayesian geostatistical modelling for spatiallyexplicit risk profiling of P. falciparum infection prevalence in a highly malariaendemic part of subSaharan Africa was used. This approach can be readily adapted to other ecoepidemiological settings for spatial targeting of control interventions. In particular, it was possible to compare different geostatistical models with a large set of covariates, including demographic, socioeconomic and environmental factors, physical access to health care and bed net usage. The results suggest that the use of nonstationary models might be justified also at smallscale areas, however further research is necessary to deepen the current understanding of the finescale spatial heterogeneity of P. falciparum. Malaria patterns are complex and the risk of infection is influenced by many other factors that were not accounted for in this study, including malaria control interventions and genetic diversity. Specifically, vector breeding sites at small scale (i.e. abundance of small water pools) may significantly influence the spatial heterogeneity in the study area [41–43]. Further analyses that apply information derived from land use maps are needed, as well as models to predict the spatial distribution of P. falciparum parasitaemia.
Notes
List of abbreviations
 ADDS:

Africa Data Dissemination Service
 AIC:

Akaike Information Criterion
 BCI:

Bayesian Credible Interval
 CI:

Confidence Interval
 DALY:

DisabilityAdjusted Life Year
 DIC:

Deviance Information Criterion
 GIS:

Geographical Information system
 GPS:

Global Positioning System
 IRS:

Indoor Residual Spraying
 ITN:

InsecticideTreated Net
 LST:

Land Surface Temperature
 MCMC:

Markov Chain Monte Carlo
 MODIS:

Moderate Resolution Imaging Spectroradiometer
 NDVI:

Normalized Difference Vegetation Index
 OR:

Odds Ratio
 RFE:

Rainfall Estimates
 WBC:

White Blood Cell
Declarations
Acknowledgements
We are grateful to the staff members of the health sanitary district of the region of Man, education officers, teachers and schoolchildren who participated in the surveys. Thanks are also addressed to the field and laboratory technicians M. Traoré, K.L. Lohourignon, B.A. Sosthène, A. Allangba, and S. Diabaté. Financial support was provided by the Novartis Foundation, the Swiss National Science Foundation to GR (project no. PBBSB109011), PV (project no. 325200118379) and JU (project no. PP00B–102883, PPOOB–119129). GR acknowledges financial support from the University of Queensland through a Postdoctoral Research Fellowship and Early Career Research Grant (project no. 2007002086).
Authors’ Affiliations
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