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Acta Tropica 121 (2012) 112– 117

Contents lists available at SciVerse ScienceDirect

Acta Tropica

jo ur nal homep age : www.elsev ier .com/ locate /ac ta t ropica

tilizing environmental, socioeconomic data and GIS techniques to estimate theisk for ascariasis and trichuriasis in Minas Gerais, Brazil

onaldo G.C. Scholtea,b,∗, Corina C. Freitasc, Luciano V. Dutrac, Ricardo J.P.S. Guimaraesc,andra C. Drummondd, Guilherme Oliveirae, Omar S. Carvalhoe

Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, P.O. Box, CH-4002 Basel, SwitzerlandUniversity of Basel, P.O. Box, CH-4003 Basel, SwitzerlandNational Spatial Research Institute/INPE São Jose dos Campos, SP, BrazilHealth State Office of Minas Gerais, Belo Horizonte, MG, BrazilResearch Center René Rachou/Fiocruz-MG, Av. Augusto de Lima, 1715 Barro Preto, CEP 30190-002 Belo Horizonte, MG, Brazil

r t i c l e i n f o

rticle history:eceived 12 July 2011eceived in revised form 11 October 2011ccepted 15 October 2011vailable online 21 October 2011

eywords:eohelminthsscariasisrichuriasiseographical information systems

a b s t r a c t

The impact of intestinal helminths on human health is well known among the population and healthauthorities because of their wide geographic distribution and the serious problems they cause. Geo-helminths are highly prevalent and have a big impact on public health, mainly in underdeveloped anddeveloping countries. Geohelminths are responsible for the high levels of debility found in the youngerpopulation and are often related to cases of chronic diarrhea and malnutrition, which put the physical andintellectual development of children at risk. These geohelminths have not been sufficiently studied. Oneobstacle in implementing a control program is the lack of knowledge of the prevalence and geographi-cal distribution. Geographical information systems (GIS) and remote sensing (RS) have been utilized toimprove understanding of infectious disease distribution and climatic patterns. In this study, GIS and RStechnologies, as well as meteorological, social, and environmental variables were utilized for the model-

ultiple regression ing and prediction of ascariasis and trichuriasis. The GIS and RS technologies specifically used were thoseproduced by orbital sensing including the Moderate Resolution Imaging Spectroradiometer (MODIS) andthe Shuttle Radar Topography Mission (SRTM). The results of this study demonstrated important factorsrelated to the transmission of ascariasis and trichuriasis and confirmed the key association between envi-ronmental variables and the poverty index, which enabled us to identify priority areas for interventionplanning in the state of Minas Gerais in Brazil.

Helminths belong to one of the most important and largestoological groups in the animal kingdom that includes humans, ani-als and plant parasites. They are called geohelminths because of

heir cycle in the environment. They are etiologic agents of humannfectious disease usually caused by people consuming their eggs oria active worm penetration through the skin. They are especiallyrevalent in underdeveloped and developing countries, where theombination of climatic factors and poor hygienic conditions facil-tates transmission (Bethony et al., 2006; Brooker et al., 2007). It isn important health human problem (WHO, 2005).

Helminths, also known as soil-transmitted helminths (STHs),re responsible for physical and intellectual delays in children,ainly in early infancy, and interfere with school performance,

∗ Corresponding author at: Department of Epidemiology and Public Health, Swissropical and Public Health Institute, P.O. Box, CH-4002 Basel, Switzerland.el.: +41 61 284 88226; fax: +41 61 284 8105.

E-mail address: ronaldo.scholte@unibas.ch (R.G.C. Scholte).

001-706X/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.actatropica.2011.10.011

© 2011 Elsevier B.V. All rights reserved.

attendance and productivity in children and adolescents (Bleakley,2003; Miguel and Kremer, 2003). Although STHs have an impor-tant impact on education, economy and public health, they arestill neglected diseases (Chan, 1997). The most severe infectionsoccur primarily in children between the ages of 5 and 15 years.The intensity and frequency of the disease reduces as age increases(Galvani, 2005). More than one billion people globally are infectedwith at least one type of STH. Ascaris lumbricoides (A. lumbricoides)and Trichuris trichiura (T. trichiura) are the most important of theSTHs (WHO, 2005; Bethony et al., 2006).

Climatic conditions have an important role in determining infec-tion rates. The prevalence of the infection is relatively low in highaltitude regions with low rainfall patterns. Hot and humid weatherpatterns are considered vital for the survival of the parasites espe-cially during the embryonic stage of the eggs. Furthermore, because

basic sanitation has been virtually nonexistent in areas of highprevalence, over many years a large population has contributedto an increase in the parasitic load and the pervasiveness of theinfection (Massara and Enk, 2004).

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Since these diseases are caused by risk factors present throughime and space, the geographic information system (GIS) and theemote sensing (RS) are powerful tools that can help elucidate therevalence of infection and the distribution of risk factors. The

oint use of GIS tools and statistic techniques allow for elaboraterediction and the creation of indication models of disease con-rol strategies with better subsequent resource optimization (Beckt al., 1997, 2000; Bavia et al., 2001; Guimarães et al., 2006, 2008,009, 2010b). Brooker and Michael (2000) demonstrated in epi-emiologic studies of helminthic infection control programs thathe GIS/RS combination is a useful tool to better understand epi-emiology aspects.

This study aims to create prediction models for cases ofscariasis and trichuriasis by utilizing social and environmentalariables and to analyze them using GIS/RS techniques and multipleegressions.

. Materials and methods

.1. Study area

Minas Gerais is the fourth largest Brazilian state, approximately90,000 km2, and has the second highest population of all Brazil-

an states, estimated at 19 million inhabitants distributed through53 municipalities. Fig. 1 shows the study area and its division

nto municipalities. The state is divided into five distinct ecolog-cal zones. For example, the north part of the state is arid, and theouth is hilly and green (IBGE, 2011).

.2. Ascariasis and trichuriasis prevalence data

Information regarding the prevalence (Pv) of ascariasis and

richuriasis in 449 districts (a dependent variable) in the state of

inas Gerais were obtained from annual reports (1997 to 2008) ofhe Schistosomiasis Control Program of the Health State Office of

inas Gerais.

Fig. 1. The study area of M

ica 121 (2012) 112– 117 113

1.3. Meteorological variables

The meteorological variables included total precipitation, min-imum and maximum temperatures in the summer (between01/17/2002 and 02/01/2002) and winter (between 06/28/2002 and07/12/2002) and were obtained by the Center for Weather Fore-cast and Climate Studies (CPTEC/INPE). The year 2002 was chosento represent meteorological data, which were considered typicalwith a small weather influence by the El Nino and La Nina weathersystems.

1.4. Social and sanitation variables

Social variables, including basic sanitation (the water andsewage system), data from the Human Development Index (HDI)for the years of 1991 and 2000 and the rural population (Rural Pop),were obtained from the Brazilian Urban Indicator System (BUIS)and the Brazilian Institute of Geography and Statistics (IBGE). Thesedata are from the census and are updated every 10 years. Thehealthcare need index (HNI) was obtained from the João PinheiroFoundation, which considers information from 49 variables relatedto the health, sanitation and the economy.

1.5. Moderate resolution imaging spectroradiometer (MODIS)

MODIS images were obtained for Minas Gerais during two sea-sons, summer (from January 17 through February 1 of 2002) andwinter (from July 28 through August 12 of 2002). MODIS imageswith a 250 m spatial resolution of the normalized difference veg-

etation index (NDVI) and the enhanced vegetation index (EVI)were used in this study and ranged from blue-, red-, near-, tomiddle-infrared bands. Four images downloaded from MODIS’website were used to cover the entire state.

inas Gerais, Brazil.

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.6. Shuttle radar topography mission (SRTM)

The Digital Elevation Model (DEM) was obtained by the Shut-le Radar Topography Mission (SRTM), creating the most complete

igure 2. Prevalence as a function of remote sensing variables: (a) observed prevalencerevalence, (d) estimated prevalence and (f) residuals for Trichuris trichiura.

ica 121 (2012) 112– 117

and high-resolution digital Earth topography data. The SRTM was

a modified radar system that flew with the Space Shuttle Endeav-our during an 11-day mission in February of 2000. It has a spatialresolution of 90 m on the equator. The altitude was obtained by

, (c) estimated prevalence and (e) residuals for Ascaris lumbricoides; (b) observed

R.G.C. Scholte et al. / Acta Tropica 121 (2012) 112– 117 115

Table 1Variables selected for the creation of the predictive model of ascariasis and its values.

Estimated parameters Standard deviation t P value Beta coefficients Std. deviation Beta

Intercepto 3.16 1.04 3.04 0.0026IP I 13.73 2.07 6.62 0.0000 0.34 0.051

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he SRTM DEM for each pair of coordinates. The local declivity waserived from the SRTM DEM according to Guimarães et al. (2008).

.7. Spectral linear mixing model (SLMM)

The SLMM is a process which creates an algorithm of imagessing fractions or proportions of each component (vegetation, soilnd shade) inside the pixel, estimated by the minimization of theum of the squared error (Shimabukuro and Smith, 1991). Theesponse of the pixel in any spectral band is assumed to be a linearombination of responses of each individual response componentaccording to Guimarães et al., 2010a).

In this study, the vegetation, soil and shade image fractions wereade by utilizing the MODIS data and the estimated values for the

eflection components (Guimarães et al., 2010a).Since the Pv data are provided by the municipalities of Minas

erais, each variable was integrated inside municipality limits, uti-izing the GIS programs (ArcGis 9.3, ESRI, Redlands, California, US)nd exported to a standard excel spreadsheet for the statisticalnalysis and modeling.

.8. Linear regression model for the estimation of the infectionate

In this study, a relationship between the Pv of ascariasis andrichuriasis and the aforementioned variables was established bytilizing multiple regression models.

The multiple linear regressions were employed based on thelobal model, in which a linear regression was established to esti-ate the disease rates throughout the state.Homogeneous and contiguous regions were determined for the

tate using biological variables and the Skater algorithm (Assunc ãot al., 2006; Martins-Bedê et al., 2009; Guimarães et al., 2010b).

The model was built utilizing the data from the 449 munici-alities, shown in Fig. 2a and b, where the Pv information wasvailable. The fitted models were then used to build the risk map forhe entire Minas Gerais state applying the model to the remaining

unicipalities.

.9. Applied model

The values for the rates of ascariasis and trichuriasis infec-

ion, environmental and social variables, including some sanitationnformation and information from the human development index,erved as input data to create the multiple regression models andstimate the infection rate. The relations between the dependable

able 2ariables selected for the creation of the predictive model of trichuriasis and its values.

Estimated parameters Standard deviation t

Intercepto 1.84 0.60

Tmin I 0.17 0.03

Tmin V −0.21 0.04 −DEC 0.06 0.009

SOMB I −0.03 0.01 −Pop Rural −0.007 0.001 −INS 0.59 0.2

6.12 0.0000 −0.30 0.0494.86 0.0000 0.24 0.0503.80 0.0002 −0.20 0.053

variable (Pv), the 69 independent social, meteorological, remotesensing and sanitation variables, were analyzed in terms of corre-lation and multicollinearity.

The logarithmical changing of the Pv variable improved the cor-relation with independent variables. The process of the variableselection first involved the variable reduction with a nonsignifi-cant correlation with the Pv at a significance level of 5%. After thisprocess, the variable selection was completed, utilizing the R2 cri-terion, registering all possible models of regression (Neter et al.,1996). The selection criterion is the identification of a subgroupwith a few variables and a coefficient of determination R2. It is sim-ilar to what we have when all variables are used in the model. Inthe regression, R2 is the closest statistical measure of the regressionline, which is close to the real points. It measures the proportionalreduction of the total variation related to the use of independentvariables.

The final statistical model was built, utilizing the STATISCA 6.0(StatSoft, Tulsa, Oklahoma, US) program based on 449 districts withsome information of Pv. The adjusted model was utilized to createthe risk map for the entire state of Minas Gerais using the applica-tion model for other districts.

2. Results

The relationship between the prevalence of ascariasis andtrichuriasis, the social variables and the meteorological and remotesensing data of municipalities in the state of Minas Gerais wasdetermined using multiple regression models, and these resultswere combined with the geographical information system data.

2.1. Ascaris lumbricoides

The matrix analysis of correlation has shown that some variablesdo not have meaningful correlations with Pv at a confidence levelof 95%, and some predictive variables are correlated to themselvesshowing that the model can be simplified.

The final model had four variables (Table 1): near infrared inthe winter (NIR W), the difference between the maximum andminimum winter temperature (dT W), the minimum summer tem-

perature (Tmin S) and the healthcare need index (HNI) with acoefficient of determination (R2) of 0.30 and statistical signifi-cance of p < 0.05. The dT W variable was correlated negativelyto the prevalence of ascariasis, while NIR W, Tmin S and HNI

P value Beta coefficients Std. deviation Beta

3.03 0.0034.90 0.0000 0.40 0.084.84 0.0000 −0.39 0.086.91 0.0000 0.35 0.056.70 0.0000 −0.35 0.054.14 0.0000 −0.24 0.062.95 0.003 0.20 0.07

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emonstrated a positive correlation. After the variable determina-ion, the final regression equation was as follows:

v = e(3,17+13,73∗(NIR W)−0,18∗(dT W)−0,16∗(T min S)+1,05∗(HNI)) − 1 (1)

This equation was utilized to estimate the Pv for all districts inhe state of Minas Gerais (Fig. 2c). Fig. 2e shows the plot of theesiduals from the difference among observed values (Fig. 2a) andhe ascariasis Pv estimation (Fig. 2c). In this figure, the dark brownrea shows the subestimate values, the light brown area shows theverestimated values and the blue area the municipalities with anccurate estimate.

.2. Trichuris trichiura

For the trichuriasis Pv, the final model had six variablesTable 2): declivity (DEC), winter shadow (SHADE W), rural popula-ion (Rural Pop), winter minimum temperature (Tmin W), summer

inimum temperature (Tmin S) and the healthcare need indexHNI), with R2 = 0.31. The SHADE W and the Rural Pop variablesere correlated negatively with the trichuriasis IR, whereas theEC, Tmin S, Tmin W and the HNI demonstrated a positive corre-

ation. After the important variable determination for the model,he final regression equation was as follows:

v = e(1,84+0,06∗(DEC)−0,04∗(SHADE W)−0,01∗(Rural Pop)+0,17∗(T min W)−0,22∗(T min S)+0,59∗(HNI))

− 1 (2)

This equation was used to estimate the T. trichiura Pv for allistricts. Fig. 2f shows the plot of the residuals from the differencemong observed values (Fig. 2b) and the estimated trichuriasis PvFig. 2d). In this figure, the dark brown areas show the munici-alities with underestimated values and the blue areas show theunicipalities with an accurate estimate.

. Discussion

Variables from many sources (social, meteorology and remoteensing) were utilized in this study. The Pv estimate equation showshat meteorological (Tmin S) and social (HNI) variables are impor-ant because they are present in both disease models. These are theame variables (meteorological and social) which Guimarães et al.2010b) suggested as important ones to use in creating a forecast

ap for Schistosoma mansoni in the state of Minas Gerais.These variables were selected based on the data about the biol-

gy of A. lumbricoides and T. trichiura. The factors that facilitateisease transmission are an ideal temperature for the egg devel-pment that varies from 28 to 35 ◦C and the living conditions ofow-income populations (Crompton and Pawlowski, 1985; Beer,971, 1973, 1976).

Ascaris lumbricoides

For a better understanding of these results, we researched theiology of helminths and found that the ideal temperature for thegg development is between 28 and 32 ◦C. Hence when the dT W isigh, the temperature variation affects egg development such thathe positive rate decreases when the Tmin S is high. An increasen humidity makes an ideal environment for egg development,

hich increases the infection rate. The NIR W variable is relatedo vegetation. Areas of vegetation in less sandy and more humidoil facilitate egg development and increase the helminth infectionate. Alternatively, the HNI is directly related to the life conditions

f the population: the more healthcare that is needed, the poorerife conditions the population will have, and therefore the infec-ion probability increases (Brooker and Michael, 2000; Hotez et al.,003).

ica 121 (2012) 112– 117

• Trichuris trichiura

The SHADE W variable is correlated with the presence of watercollection. The T. trichiura eggs are not resistant to water immersion(Brooker and Michael, 2000) making egg development difficult andreducing the possibility of transmission.

The Rural Pop variable has shown a negative correlation withtrichuriasis because the rural population is diffuse and the presenceof parasites is related to population concentration (Crompton andSavioli, 1993; Phiri et al., 2000). In summary, in the areas with alarge rural population, the trichuriasis infection rate is reduced.

High and low temperatures limit T. trichiura egg evolution, asthe ideal temperature for development is between 28 and 35 ◦C.High temperature kills eggs, while low temperature postponestheir development. Therefore, a high Tmin S variable increases thehumidity, making the hot and humid environment perfect for theegg development, and therefore increasing the infection rate. Alter-natively, when the Tmin W is low, the egg evolution is slow, whichincreases the parasite’s life cycle and consequently reduces theinfection rate. (Seamster, 1950; WHO, 1967; Appleton and Gouws,1996; Appleton et al., 1999; Brooker and Michael, 2000; Hotez et al.,2003).

The DEC variable is related to the water dispersion speed andthe rainfall accumulation. Lower declivity lowers the probability ofinfection, perhaps because these conditions make the eggs not asviable.

The T. trichiura infection has fertile territory in the poorest com-munities with poor living conditions, so the HNI is a good indicatorfor the occurrence of the parasite. A higher index number correlateswith a higher trichuriasis infection rate.

Some questions related to the nature and the precision of Pvmust be considered when the results are observed. The majority ofthe Pv data were obtained before GPS equipment was available forthis purpose. The information is on a district scale, and most dis-tricts are in the north and northeast of the state. Therefore, the datado not show the spatial distribution of the parasites and the exactgeographical locations. These issues were discussed by Guimarãeset al. (2006) and they may have affected and masked the infectionrate correlations with the independent variables.

First, the results of this study reveal important factors related tothe disease transmission and confirm the relevance of basic sanita-tion (proper treatment of effluents and potable water supply) andhealth education as measures which could reduce the probabilityof infection. (Massara and Enk, 2004). Furthermore, the approachhere can be useful in the identification of priority areas for interven-tion, aiding in making proper decisions and more efficiently usingthe resources of the economy. Therefore, even with a low coeffi-cient of determination, the combination of the GIS/RS and statisticaltechniques, used with socioeconomic, meteorological and remotesensing data allows the identification of regional factors related toascariasis and trichuriasis transmission and facilitate the delim-itation and classification of areas according to the transmissionprobability of these parasites. It is important to conduct georefer-encing studies of the prevalence of geohelminths in districts wherethere are no such data, to improve the modeling precision for moreaccurate estimates and use the application in the identification ofpriority areas on a larger scale.

The database created by this project allows it to be used in othermodeling studies of STHs or for other diseases.

The current priority in this technique is the improvement ofaccuracy in geolocation and in the accuracy of risk estimation. Themethod of building the field data registry has been optimized with

the introduction of cutting-edge geographical location equipmentmaking a more precise determination of a possible case event. It isa great resource for the development of risk models with a betterspatial resolution.

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cknowledgements

CNPq (300679/2011-4, 384571/2010-7, 302966/2009-9,08253/2008-6), FAPEMIG (EDP 1775/03, EDT 61775/03, CRA070/04), NIH-Fogarty (5D43TW007012), FIC Training Grant1D43TW006580).

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