Spatial distribution of malaria problem in three regions of Ethiopia
© Ayele et al.; licensee BioMed Central Ltd. 2013
Received: 17 December 2012
Accepted: 11 June 2013
Published: 17 June 2013
The transmission of malaria is the leading public health problem in Ethiopia. From the total area of Ethiopia, more than 75% is malarious. The aim of this study was to identify socio-economic, geographic and demographic risk factors of malaria based on the rapid diagnosis test (RDT) survey results and produce the prevalence map of the area illustrating variation in malaria risk.
This study accounts for spatial correlation in assessing the effects of socio- economic, demographic and geographic factors on the prevalence of malaria in Ethiopia. A total of 224 clusters of about 25 households each were selected from the Amhara, Oromiya and Southern Nation Nationalities and People’s (SNNP) regions of Ethiopia. A generalized linear mixed model with spatial covariance structure was used to analyse the data where the response variable was the presence or absence of malaria using the RDT.
The results showed that households in the SNNP region were found to be at more risk than Amhara and Oromiya regions. Moreover, households which have toilet facilities clean drinking water, and a greater number of rooms and mosquito nets in the rooms, have less chance of having household members testing positive for RDT. Moreover, from this study, it can be suggested that incorporating spatial variability is necessary for understanding and devising the most appropriate strategies to reduce the risk of malaria.
KeywordsMixed model Rapid diagnostic test Spatial statistics Variogram Kriging
Malaria is a life-threatening disease affecting the world’s most under-developed countries and regions where basic healthcare infrastructure is lacking  as well some developed countries. Malaria is a major cause of morbidity and mortality in Africa, especially in sub-Saharan African countries . It is a leading cause of death amongst children in many African countries . With 68% of the total population of Ethiopia living in areas at risk of malaria , it is a major public health problem and for many years the prime cause of illness and death [3, 4]. From the total population of Ethiopia (77,127,000 in 2007), more than 50 million people are at risk from malaria . In general, 4–5 million people are affected by malaria annually [6, 7].
Epidemics of malaria are relatively frequent [8, 9] involving highland or highland fringe areas of Ethiopia, mainly areas 1,000-2,000 meters above sea level [10–12]. Notably this altitude covers 48% of the regions of Amhara, Oromiya and Southern Nations Nationalities and People’s regions of Ethiopia. Malaria epidemics have serious consequences for Ethiopia’s subsistence economy as the malaria transmission peaks during the major harvesting seasons. To control the risk of malaria, early diagnosis and prompt treatment is one of the key strategies. To diagnose malaria, clinical diagnosis is the most widely used. But, laboratory facilities are not available in all areas of the country [13, 14]. The standard method to diagnose malaria is microscopy. However, this form of diagnosis is not accessible or affordable in most peripheral health facilities. The recent introduction of rapid diagnostic tests (RDT) for malaria is a significant step forward in case detection, timely treatment and management, and reduction of unnecessary treatment. RDT could be used in malaria diagnosis during population-based surveys and to provide immediate treatment based on the results.
RDTs offer the potential to extend accurate malaria diagnosis to areas where microscopy services are not available such as in remote locations or after regular laboratory hours. Rapid malaria diagnostic tests have been developed in the lateral flow format . These tests use finger-stick or venous blood, which takes only 10 to 15 minutes to complete, and do not require a laboratory. Non-clinical staff can easily learn to perform the test and interpret the results .
It is essential to identify the socio-economic and demographic risk factors associated with the prevalence of malaria using data obtained from the rapid diagnosis test. Such a study of the identification of the socio-economic and demographic risk factors is helpful in identifying households who have a critical need for intervention. In previous studies, Ayele, Zewotir and Mwambi (2012) have concluded that malaria problem in Amahara, Oromiya and SNNP regions of Ethiopia are associated with key socio-economic, demographic and geographic factors, in particular it was noted that poverty levels of households are highly associated with the risk of malaria. Nevertheless the spatial distribution of malaria was not considered or investigated . Though identification of the household characteristics is essential for grass root level intervention, the government goals and targets are focused on achieving malaria eradication/reduction within specific geographical areas. Such studies are limited, and hence the conception of this study. Therefore, the objective of this study is to undertake a statistical analysis of malaria incidence. This will identify important socio-economic, demographic and geographic variables associated with the disease and ultimately a prevalence map of the area illustrating variations in malaria risk.
Before testing for malarial parasites, consent was obtained from the participants. To collect the sample, finger-prick blood was collected from the participants for the malaria rapid diagnostic test. The test used is known as ParaScreen which is capable of detecting both Plasmodium falciparum and other Plasmodium species. Participants with positive rapid tests were immediately offered treatment according to national guidelines.
Variables of interest
The outcome of interest is the malaria rapid diagnosis test (RDT) result. RDTs assist in the diagnosis of malaria by detecting evidence of malaria parasites in human blood and are an alternative to diagnosis based on clinical grounds or microscopy, particularly where good quality microscopy services cannot be readily provided. Thus, the response variable is binary, indicating whether or not a person is positive for malaria using the RDT.
The independent variables or covariates were the baseline socio-economic status, demographic and geographic variables including gender, age, family size, region, altitude, main source of drinking water, time taken to collect water, toilet facilities, availability of electricity, radio and television, total number of rooms, main material of the room’s walls, main material of the room’s roof, main material of the room’s floors, incidence of anti-mosquito spraying in the past 12 months, use of mosquito nets and total number of nets. Malaria test (RDT result), age and sex were collected at individual level. Altitude, main source of drinking water, time taken to collect water, toilet facilities, availability of electricity, radio, television, total number of rooms, main material of the room’s walls, main material of the room’s roof, main material of the room’s floor, use of anti- mosquito spray in the past 12 months, use of mosquito nets and total number of nets were all collected at household level.
The statistical model
The distribution of malaria is nonrandom across a landscape in areas of higher or lower transmission intensity and malaria risk. The transmissions are separated by greater or lesser distances from each other. Based on geographical aggregation, there are two distinct levels. These are, the focal unit of malaria transmission, the area over which human malaria is actively transmitted originating from a specific aquatic breeding site and the household or other reasonably identified point of contact between a small group of humans and mosquito vectors. The baseline household cluster malaria survey which was conducted by The Carter Center from December 2006 to January 2007 includes the geographical locations of the reference for each household. Therefore, it is of interest to know whether the data display any spatial autocorrelation. Furthermore, it is important to check whether surveys that are near in space have malaria prevalence or incidence that is similar with the surveys that are far apart. This is important because spatially correlated data cannot be regarded as independent observations. If the analysis does not take account of the correlation structure of the data, the estimates obtained from the analysis may be inaccurate because of the underestimated standard errors. Therefore, the objective of this study is to undertake statistical analysis of malaria incidence to identify important socio-economic, demographic and geographic variables associated with the disease and to produce prevalence maps of the area illustrating the variation in malaria risk using spatial statistics analysis. Spatial statistics can be divided into three methods. These are: point pattern analysis, methods for lattice data and geostatistics [20, 21]. Point referenced data is often called geocoded or geostatistical data. Areal data is often called lattice data. Some spatial data sets feature both point and areal-level data. Point pattern data: The response is often fixed (occurrence of the event), and only the locations where it occurs are thought of at random. Of these, the geostatistical approach is most relevant to epidemiological analysis which is conducted at the landscape scale and based on remote sensing [22–24].
A common approach to integrate spatially correlated data with the random effects and proceed with maximum likelihood based approaches for estimating the covariate and covariogram parameters, is based on the theory of generalized linear mixed models (GLMM). Using GLMM, numerical approximation can be implemented [20, 25].
where X and Z are the design matrix. The error term accommodates over-dispersion relative to the mean-variance relationship implied by the distribution under consideration. The random effect at location si, α ∼ Gau(0, ∑ α (θ)) and , with spatial correlation is parameterized by θ in ∑ α (θ) . Note that s i is just one location. s = (s l , ,s k )’ denotes a vector of k locations with variance-covariance matrix .
In statement (2), the variance of s 1 and s 2 is through their difference s 1 -s 2 , and the process which satisfies this property is called intrinsically stationary. The function 2y(.) is called the variogram and y(.) the semivariogram.
- 4.Exponential-power form:
Here 0 < p ≤ 2. This form generalizes both the exponential and Gaussian forms, and forms the basis for the families of spatial covariance functions introduced by Sacks et al. in 1989 . However, in generalizing the results from one dimension to higher dimensions, these authors used a product form of covariance function in preference to constructions based on isotropic processes .
Modelling spatial data is not only useful for identifying significant covariates but for producing smooth maps of the outcome by predicting it at unsampled locations. Spatial prediction is usually referred to as kriging. Kriging is an optimal interpolation based on regression against observed values of surrounding data points, weighted according to spatial covariance values. Interpolation refers to an estimation of a variable at an unmeasured location from observed values at surrounding locations . Kriging has some advantages. These advantages are that it
helps to compensate for the effects of data clustering, assigning individual points within a cluster less weight than isolated data points,
gives an estimate of estimation error (kriging variance), along with an estimate of the variable,
ensures availability of estimation error which provides a basis for stochasticity,
allows simulation of possible realization.
The spatial prediction which is called kriging can statistically be defined as follows.
With Σ 11 = E(UU’), Σ 00 = E(U o U’ 0 ) and . The mean of the Gaussian distribution in (4) is the classical kriging estimator .
Where P(β, U, σ2, ϕ|Y) is the posterior distribution of the parameters obtained by the Gibbs sampler or the sampling importance re-sampling (SIR) approach. Simulation-based Bayesian spatial prediction is performed by consecutive draws of samples from the posterior distribution, the distribution of the spatial random effects at new locations and the Bernoulli-distributed predicted outcome. The maximum likelihood predictor (3) can be viewed or interpreted as the Bayesian predictor (5), with parameters fixed at their maximum-likelihood estimates. In contrast to Bayesian kriging, classical kriging does not account for uncertainty in estimation of β and the covariance parameters.
The data was analysed by fitting a generalized linear mixed model (GLMM) using SAS 9.2 PROC GLIMMIX.
Analysis and results
Using the identified thirteen main effects and six two-way and three-way interaction effects  several covariance structures including SP(EXP) (Exponential), SP(EXPA) (Anisotropic Exponential), SP(EXPGA)( (2D Exponential, Geometrically Anisotropic), SP(GAU) (Gaussian), SP(GAUGA)( (2D Gaussian), (Geometrically Anisotropic), SP(LIN) (Linear), SP(LINL) (Linear Log), SP(MATERN)(Matérn), SP(MATHSW)(Matérn (Handcock-Stein-Wallis)), SP(POW) (Power), SP(POWA) (Anisotropic Power), SP(SPH) (Spherical) and SP(SPHGA)( (2D Spherical, Geometrically Anisotropic) were fitted but SP(GAU) (Gaussian) was found to be the best spatial covariance structure for the model .
For these tests, the null hypothesis states that the spatial distribution of feature values is the result of random spatial processes. The result from Moran’s (Z value = −40.4 and p – value < .0001) and Geary's c (Z value = −11.2 and P-value < .0001) tests indicate that the spatial distribution of feature values is not the result of random spatial processes. The Z values are negative for both Moran’s and Geary’s C tests. This indicates that the spatial distribution of high values and low values in the dataset is more spatially dispersed than would be expected if underlying spatial processes were random. A dispersed spatial pattern often reflects some type of competitive process, i.e., a feature with a high value repels other features with high values; similarly, a feature with a low value repels other features with low values. The observed spatial pattern of feature values could not very well be one of many possible versions of complete spatial randomness.
In general, from Figure 3, it is possible to distinguish three main features. The first one is the Y-axis well above zero, indicating the possible presence of a nugget effect. Moreover, the shapes of the semivariogram up through distances in the low 40s have roughly the shape of a spherical covariance model. Besides these, the semivariogram values are extremely high for the largest distances.
Socio-economic, demographic and geographic of effects on malaria RDT test for main effects
Gender (ref. male)
Region (ref. SNNP)
Main source of drinking water (ref. protected water)
Time to collect water (ref. > 90 minutes)
< 30 minutes
between 30 to 40 minutes
between 40–90 minutes
Toilet facility (Ref. No facility)
Toilet with flush
Availability of electricity (ref. no)
Availability of television (ref. no)
Availability of radio (ref. no)
Number of rooms/person
Main material of room's wall (ref. cement block)
Main material of room's roof (ref. corrugate)
Stick and mud
Main material of room's floor (ref. earth/Local dung plaster)
Anti- mosquito spraying
Use of mosquito nets (ref. no)
Number of months room sprayed
Number of nets/person
Socio-economic, demographic and geographic of effects on malaria RDT test for interaction effects
Gender and main source of drinking water (ref. Male & protected water)
Female and tap water
Female and unprotected water
Gender and material of room's floor (ref. Male and earth/Local dung plaster)
Female and cement
Female and wood
Age, gender and main source of drinking water (ref. Male & protected water)
Female and tap water
Female and unprotected water
Age, gender and availability of electricity (ref. Male & yes)
Female and no
The first priority in the acute stage of a malaria epidemic is prompt and effective diagnosis and treatment. Having well-planned and timely vector control can significantly contribute to a reduction in the risk of infection and consequently in saving lives. Vector control must be proactive and should be implemented at an early stage of epidemic development. Timing depends on effective early warning and early detection. Because of this, the government of Ethiopia has developed strategies related to human resource development, monitoring, and evaluation to control malaria and reduce the hardship it causes. Based on this strategy, the main objective of the government is to make those areas with historically low malaria transmission, malaria free and a near zero malaria transmission in the remaining malarious areas of the country . Based on some studies which were conducted previously, malaria was regarded as a disease of the poor or a disease of poverty . Looking at the global distribution of malaria in the world suggested that the concentration of the disease is in the world’s poorest continents and countries. Accurate information on the distribution of malaria in epidemic-prone areas on the ground permits interventions to be targeted towards the transmission and high-risk locations and households. Such targeting greatly increases the effectiveness of control measures but the inadvertent exclusion of these locations causes potentially effective control measures to fail. The computerized mapping and management of location data assists the targeting of interventions against malaria at the focal and household levels, leading to improved efficacy and cost-effectiveness of control.
As the distribution of malaria infection suggests, it is important to understand the relationship between malaria and poverty. This relationship is important to enable the design of coherent and effective policies and tools to tackle the problem [46, 47]. As is already known, poverty is related to socio-economic factors. Therefore, it is important to identify those factors which are also related to the risk of malaria. Based on these facts, the findings from the current study show that the following socio-economic factors are related to the risk of malaria: main source of drinking water, time taken to collect water, toilet facilities, availability of radio, total number of rooms per person, main material of the room’s walls, main material of the room's roof, main material of the room's floor, spraying of anti- mosquito, use of mosquito nets, total number of persons per net. Besides socio-economic factors, there are demographic and geographic factors which also have an effect on the risk of malaria. These include gender, age and family size. In addition to the main effects there were interactional effects between the socio-economic, demographic and geographic factors which also influenced the risk of malaria. Most notable of these were the interaction between main source of drinking water and main material of the room's roof, time taken to collect water and main material of the room's floor, age and gender, gender and availability of electricity, gender and main material of the room's floor, age, gender and main source of drinking water; and age, gender and availability of electricity.
Spatially correlated data cannot be regarded as independent observations. Therefore, ignoring the spatial variability might lead to an inaccurate estimation of parameters. Accordingly, unlike Ayele, et al. (2012), the spatial correlation structure was considered and the significance of the variables was checked and predictions of the malaria risk levels for the sampled areas were produced. A useful way of providing up to date information is in the use of GIS-based management systems. This method helps to address effective malaria vector control and management. Therefore, the spatial distribution of malaria incidence was one of the points which were important for such GIS studies.
Spatial clustering of malaria is almost predictable as human populations generally live in spatial clusters rather than in random distributions of space. Disease which is highly correlated to socio-economic variables is likely to be spatially clustered. Therefore, the model explains some of the spatial patterns of malaria risk for Amhara, Oromiya and SNNP regions of Ethiopia. Moran’s and Geary's C tests were used to test for randomness [37–41]. The interest was to test if the spatial distribution of feature values is the result of random spatial processes. However, the test favors that the spatial distribution of feature values is not the result of random spatial processes. Moreover, the spatial distribution of high values and low values in the dataset is more spatially dispersed than would be expected. A dispersed spatial pattern often reflects some type of competitive process, i.e. a feature with a high value repels other features with high values; similarly, a feature with a low value repels other features with low values.
Malaria distribution is mainly related to the rainy seasons in Ethiopia. Therefore, understanding the nature of the Ethiopian climate is important. According to the Ethiopian National Meteorological Services Agency (NMSA), climates in Ethiopia can be divided into four climatic zones based on the pattern of rainfall. There are: the two-season type (the western half of Ethiopia) which is divided into district wet and dry seasons; bi-two season type (the south and southern of Ethiopia) is characterized by double wet seasons that occur between March to May and September to November with two dry seasons in between; the undefined season (dry northern part of the Ethiopian Rift Valley) mostly has irregular rainfall between July and February without any defined season; and the three-season type (central and south western Ethiopia). The average annual rainfall in the highlands of Ethiopia is above 1000 mm a year and it rises to 2000 mm and 3000 mm in the wet south western parts of Ethiopia. Therefore, the three regions have almost similar rainy months. Including the climate information into the analysis is important . Since the climatic information is not included in the baseline household cluster malaria survey, this information will be included for future study.
Therefore, the results of this study provide evidence on the spatial distribution of socio-economic, demographic and geographic risk factors in the occurrence of malaria. This forms the basis for this research. Therefore, the utilization of socio-economic, demographic and geographic data on malaria rapid diagnosis test, including the information on the spatial variability, clarifies the effects of these factors. From the study it was observed that residents living in the SNNP region were found to be more at risk of malaria than those living in Amhara and Oromiya regions. Similarly, houses which were treated with anti- mosquito spray were less likely to be affected by malaria. However, a major challenge in the control of malarial infection was found to be in the type of toilet facilities available in the household. From the results, it was observed that individuals living in households which had no toilet facilities were more likely to be positive for malaria diagnosis tests. Furthermore, positive malaria diagnosis rates decreased with age and the risk of malaria increased per unit increase in family size. Generally, malaria parasite prevalence differed between age and gender, with the highest prevalence occurring in children and females.
From the findings of this study, it can be suggested that having toilet facilities, access to clean drinking water and the use of electricity offers a greater chance of knowing whether or not an individual in the household is at risk of malaria or not. In addition to this, using mosquito nets and spraying anti- mosquito treatment on the walls of the house were also found to be a way of reducing the risk of malaria. Similarly, having a cement floor and corrugated iron roof was found to be one means of reducing the risk of malaria. Based on the findings, different types of housing materials have an influence on the risk of malarial transmission with those houses constructed of poor quality materials having an increased risk. Moreover, the presence of particular structural features, such as bricks, that may limit contact with the mosquito vector, also helps to reduce infection. The risk of malaria therefore, is higher for households in a lower socio-economic bracket than for others who may enjoy a higher status and who are able to afford to take measures to reduce the risk of transmission. Therefore, with the correct use of mosquito nets, anti- mosquito spraying and other preventative measures, like having more rooms in a house, the incidence of malaria could be decreased. In addition to this, the study also suggests that the poor are less likely to use these preventative measures to effectively counteract the spread of malaria. To provide clean drinking water, proper hygiene and maintaining the good condition of a house is essential in controlling the transmission of malaria. With other control measures, including creating awareness about the use of mosquito nets, anti- mosquito sprays and malaria transmission, the number of malaria cases can be reduced. Furthermore, spatial statistics studies significantly contribute to the understanding of the distribution of malarial infections. The use of spatial statistics analysis is effective in monitoring and identifying high-rate malaria affected regions and helpful when implementing preventative measures. Finally, studies incorporating spatial variability are necessary for devising the most appropriate methodology for remedial action to reduce the risk of malaria.
The ethical protocol received approval from the Emory University Institutional Review Board (IRB 1816) and Amhara, Oromiya and SNNPR regional health bureaux. Informed consent was sought in accordance with the tenets of the declaration of Helsinki.
We thank, with deep appreciation, Amhara RHB and The Carter Center, for providing and giving permission to use the data for this study.
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