RESEARCH

Prediction of high-risk areeconomic indicators

iathremote sensing data. We applied classification andls. Performance of the models was assessed by means ofmog Cpsi

living conditions [2,3]. Parnaba e Poti, at a 55 south latitude and 4248 west

INTERNATIONAL JOURNAL OF HEALTH GEOGRAPHICS

Almeida and Werneck International Journal of Health Geographics 2014, 13:13http://www.ij-healthgeographics.com/content/13/1/13the years of 1991 and 2000, respectively.e 6 andar/bloco E, Maracan, CEP 20550-013 Rio de Janeiro, BrazilFull list of author information is available at the end of the articleThe spatial distribution of urban VL is markedlyheterogeneous, which may lead to a substantial increasein transmission levels [3]. In this situation, focusingintervention on high risk areas might be an efficientstrategy to reduce transmission rates [4].

longitude. Teresina has a population of 793,915 inhabitantsand population density of 444.2 inhabitants/km [2]. Ithas a tropical sub-humid climate, with high registeredtemperatures throughout the year, varying from 22C to40C. Rainy season is mainly from January until April.

Study designThis is an ecologic study, in which the units of analysis arethe 430 and 653 urban census tracts (CT) of Teresina for

* Correspondence: asasobral@gmail.com1Instituto de Medicina Social, Universidade do Estado do Rio de Janeiro, RuaSo Francisco Xavier, 524, Pavilho Joo Lyra Filho, 7 andar/blocos D e E,occurrence of VL in urban areas, the results of this study showed that the development of a predictive model for VLmight be feasible and useful for guiding interventions against the disease, but it is still a challenge as demonstrated bythe unsatisfactory predictive performance of the model developed.

Keywords: Leishmaniasis, Predictive models, Remote sensing

IntroductionSince the 1980s, visceral leishmaniasis (VL) had itsepidemiological profile modified in Brazil; no longerbeing characterized as a predominantly rural disease,but established in the urban environment as well [1].The introduction, propagation and dissemination of

VL in urban settings is associated to multiple andcomplex conditions, such as environmental changesdue to migration movements, disorderly occupation ofcitys outskirts, high population density, and inadequate

The objective of the present study is to characterizeand predict high risk areas for the occurrence of VL inTeresina, Piau, based on socioeconomic indicators andenvironmental data, obtained through remote sensing.

MethodsArea of studyTeresina, capital of Piau state, in the northeast region ofBrazil, is located between the confluence of the riversleishmaniasis using socioand remote sensing dataAndra S Almeida1* and Guilherme L Werneck1,2

Abstract

Spatial heterogeneity in the incidence of visceral leishmanplanning control actions for the disease. The objective ofleishmaniasis (VL) based on socioeconomic indicators andregression trees to develop and validate prediction modesensitivity, specificity and area under the ROC curve. Thecensus tracts (CT) with different probabilities of containinpresented, respectively, in the validation and learning sam66%, and area under the ROC curve of 83% and 66%. Con 2014 Almeida and Werneck; licensee BioMeCreative Commons Attribution License (http:/distribution, and reproduction in any mediumDomain Dedication waiver (http://creativecomarticle, unless otherwise stated.del developed was able to discriminate 15 subsets ofT with high risk of VL occurrence. The modelles, sensitivity of 79% and 52%, specificity of 75% anddering the complex network of factors involved in thesis (VL) is an important aspect to be considered inis study was to predict areas at high risk for visceralOpen Access

as for viscerald Central Ltd. This is an Open Access article distributed under the terms of the/creativecommons.org/licenses/by/2.0), which permits unrestricted use,, provided the original work is properly credited. The Creative Commons Publicmons.org/publicdomain/zero/1.0/) applies to the data made available in this

(a

Almeida and Werneck International Journal of Health Geographics 2014, 13:13 Page 2 of 7http://www.ij-healthgeographics.com/content/13/1/13Data and variablesResponse variableThe average annual incidence rates of VL in the CTwere calculated for the periods of 19931996 and

Figure 1 Average annual incidence rate of visceral leishmaniasis inTeresina, Piau, Brazil.20002006. Census tracts with rates above the 3rd

quartile (>0.62 cases per 1000 person-years for theperiod of 19931996 and >0.24 cases per 1000person-years for the period of 20012006) were classifiedas high risk and the ones that did not meet the criteriaas low risk (Figure 1).

Predictor variablesSocioeconomic and demographic variables were ob-tained from the Demographic Census of 1991 and2000. The 22 indicators analyzed were categorizedaccording to their quantile distribution, 19 of themwere categorized by quartiles, two by the median andone by tertiles (Table 1).The environmental classification was obtained through

the extraction of land coverage characteristics of Tere-sina using a Landsat 5 Thematic Mapper (TM) scene ofAugust 1990 and a Landsat 5 Thematic Mapper (TM)scene of June 2003.

Satellite image classificationBands 3 (red) and 4 (near-infrared) were used forthe image classification, due to larger spectral differ-ences [5].The image classification, performed with the software

Definiens Developer 7.0, encompassed two basic steps:multiresolution segmentation and algorithm classificationusing fuzzy and Boolean logics. This approach uses acombination of spectral, textural, and contextual/topologicinformation [6].

) 19931996 and in (b) 20012006, in the census tracts ofThe rules used to segment and classify the image from1990 were applied to the image from 2003.The environmental indicators for both analyzed periods

were built by means of calculating the proportion ofeach thematic class in each census tract (Table 2).This calculation was performed using the programLEGAL in the software SPRING (National Institute ofSpatial Research - INPE).

Data analysisThe data from the first period (19931996) was used asa learning or sample for the development of the predictivemodel and the second period (20012006) as a validationsample.The CART algorithm (Classification and Regression

Trees) was used to attain the predictive model for CTsat high risk for VL occurrence [7]. Generally CARTgenerates a very large tree which shows a minimumnumber of classification errors, but it is a modelexcessively adjusted to the data with a limited capacity ofgeneralization. Therefore, the tree has to be reduced(trimmed). To get a smaller tree (smaller number ofterminal nodes, or leaves) it was initially establisheda restriction of a minimum of 20 observations to beincluded before a split was attempted and a minimumof 10 observations in a terminal node. Subsequently,

Almeida and Werneck International Journal of Health Geographics 2014, 13:13 Page 3 of 7http://www.ij-healthgeographics.com/content/13/1/13Table 1 Socioeconomic indicators selected for analysis

PMENILLITE1 Percentage of illiterate men

PPOPILLITE1 Percentage of illiterate population

AVERAINCOME1 Average nominal income of head of the household

RINCOME1 Income ratio - Ratio between total income ofupper decile/total income of the poorest 40%

RSEX1 Ratio between men population/womenpopulation * 100

RDEPEND1 Ratio between persons from 0 to 14 and 60 ormore years old /15 to 59 years old * 100

PYOUNG5YEARS1 Percentage of the population that is younger than5 years old

PPOORHEAD1 Percentage of heads of households with incomeby means of the graphic inspection of the relation betweenthe number of terminal nodes of the tree and gainson the classification homogeneity, a tree with 15 nodeswas concluded to be most adequate.To calculate accuracy measures, the cutoff point of

25% to the probability of finding high risk censustracts on the terminal nodes of the tree was used,which simultaneously maximized the sensitivity andspecificity, with no important decrease of the globalaccuracy.CART models were implemented on the software

Splus 4.5. The evaluatiton of the performance of the modelswas done based on the calculations of the sensitivity,specificity, accuracy and area under ROC curve indicators;estimated in the software Stata 11.0.

up to 1/2 Brazilian minimum wage (MW)

PINCOMHEAD3MW1 Percentage of heads of households with incomeup to 3 Brazilian minimum wage (MW)

PILLITEHEAD1 Percentage of heads of households that are literate

PILLITEMENHEAD1 Percentage of heads of households that are men

PHEADWLESS31 Percentage of heads of households with less than3 years of schooling

P HEADWLESS71 Percentage of heads of households with less than7 years of schooling

P3RESHOUSE1 Percentage of households with up to 3 residents

P4 RESHOUSE1 Percentage of households with up to 4 residents

P5 RESHOUSE1 Percentage of households with up to 5 residents

PHOUSEWSAN1 Percentage of households without sewage system

PHOUSEWITHSAN2 Percentage of households with sewage systemconnected to the public network

PHOUSEWATER3 Percentage of households with water supplyconnected to the public network

PHOUSEGARBA2 Percentage of households with garbage collection

MEANPEOPLE1 Mean number of persons per household

R1 Rate of population growth1Categorized according to the quartile of the distribution.2Categorized by the median.3Categorized according to the tercile of the distribution.ResultsFor the period of 19931996, more than 70% of the CTreported VL cases, having maximum and medianincidence of 4.54 and 0.29 cases per 1000 inhabitantsrespectively. On the second period analyzed, around50% of the CT reported VL cases, with maximum andmedian incidence of 5.91 and zero cases per 1000inhabitants respectively. The satellite image classificationresults indicate the expansion of the urban area towardsthe outskirts of the city, where there was more vegetationcoverage (Figure 2).The model developed was capable of discriminating

15 CT sets (Figure 3), with different probabilities ofcontaining CTs at high risk for VL occurrence. Thisfigure shows the 15 sets of CT corresponding to theterminal nodes (in red) and inside each of them theprobabilities (P) of the existence of high risk CT andthe number of CT (N).

Table 2 Environmental indicators selected for analysis

WATER Proportion of the census tract area covered by watercollections (WATER CAT: 010; 10100)

DENSEVEG Proportion of the census tract area covered by densevegetation (DENSEVEG CAT: 01; 110; 10100)

UNDERGROWTH Proportion of the census tract area covered bypasture and shrubs (UNDERGROWTH CAT: 010;1020; 20100)

DENSEURB Proportion of the census tract area characterized asresidential with little vegetation (DENSEURB CAT: 010; 1040; 4080; 80100)

GREENURB Proportion of the census tract area characterizedas sparse residential with much vegetation(GREENURB CAT: 010; 1040; 4090; 90100)

EXPOSOIL Proportion of the census tract area covered by baresoil dirt, mud, sand (EXPOSOIL CAT: 01; 12; 24;46; 610; 10100)The subset with the lower probability of containingCTs at high risk for VL occurrence (3.9%) includedthose CTs with the percentage of literate heads of thehousehold higher than the median (>64.2%) and withthe income ratio smaller than the median (2.49%).The subset with the highest probability of containingCTs at high risk for VL occurrence (92%) encompassedCTs with percentage of literate heads of the householdbelow than the median (64.2%), with a larger areacovered with dense vegetation, with a percentage ofhousehold with up to 3 residents above the third quartile(>31.6%). The other 13 subsets presented the probabilityof containing CTs at high risk for VL occurrence varyingfrom 5.6% to 82%.Table 3 shows the models performance on the learning

(19931996) and validation (20012006) samples interms of area under the ROC curve, sensitivity, specificity,accuracy, positive and negative predictive value. The modelwith 15 terminal nodes (Figure 3) had 79% sensitivity, 74%

fr

Almeida and Werneck International Journal of Health Geographics 2014, 13:13 Page 4 of 7http://www.ij-healthgeographics.com/content/13/1/13specificity, 75% global accuracy and 83% area underROC curve. When applied on the validation sample

Figure 2 Classification of land coverage results in Teresina, derived(a) and 2003 (b). Piau, Brazilwith the same prediction cutoff (25%), it presented a52% sensitivity, 66% specificity, 62% global accuracy,and 66% area under the ROC curve (Table 3). ROCcurves figures for the validation and learning samplesare provided in Additional file 1.

DiscussionThe results of this study reinforce the notion that thespatial heterogeneity found in the occurrence of thedisease is directly related to the living conditions ofthe population and environmental characteristics ofhousehold neighborhood [3]. However, though theserelations were possible to be characterized in this study,the validation sample results were not fully satisfactory,indicating that the prediction of areas at high risk for VLincidence is a more complex challenge than the simpleidentification of associations between environmental andsocioeconomic factors and disease incidence, which hasbeen already shown in Brazil and in the world [8-14].A series of potential explanations for the deficient

predictive ability of the model developed could beidentified. First, the time interval between both periodsanalyzed implied in substantial modifications on thedimension and structure of the geographical area understudy, including the incorporation on the validationsample of areas that were considered rural in the period of19931996. Second, the data used to derive the predictivemodel refers to an epidemic period, while the validationsample entails endemic years [12,15]. It is reasonable to

om the image processing of the satellite Landsat 5 TM of 1990suppose that in epidemic situations, due to the typicallarge magnitude of transmission during these periods, thedisease spreads more largely in the geographic space,affecting population subsets that could have eventuallybeen spared in endemic periods. However, having inmind that the VL was only introduced in Teresina inthe beginning of the 1980s triggering two epidemics(19811985 and 19931996), there is still no set ofcomplete data for a typical endemic period that notof 20012006. An alternative would be to restrict theanalysis strictly to the years between 2001 and 2006,but that would bring difficulties related to the smallnumber of annual cases observed in this period.Third, the unsatisfactory performance of the model

could be due to positive quantitative variations of thesocioeconomic indicators, even if the distribution quartileswere used as cutoff points. Since the 1980s in Brazil therehas been an improvement on the social and economicindicators. For example, a historical series analysis sincethe mid-1970s shows a substantial and unequivocal fall ofinequality from 2001 to 2004, this last year having thesmallest income inequality of the period analyzed[16]. In Piau the situation is not different, having occurredan increase of 12% of the average income (19852006),contributing to the increase in the human developmentindex from 0.57 to 0.70 (19912005) and to the reductionof the illiteracy rate from 41.7% to 23.3% (19912007)

te h0

Almeida and Werneck International Journal of Health Geographics 2014, 13:13 Page 5 of 7http://www.ij-healthgeographics.com/content/13/1/13% of illiteraN=43

64,2% = Q2[17,18]. A possible alternative to minimize this problemwould be employing a categorization of the indicatorsby strata of homogeneous areas according to livingconditions [19,20].The VL transmission niches in urban environments

not only present a heterogeneous distribution, but alsoconstitute areas with varied landscape and epidemiologiccharacteristics, where distinct forms of occupation and

% of CT with area covered by dense vegetation

N=215

% of CT with area covered by water collections

% of CT by exposed soil

% up to 5 residents per household

% of CT characterized by sparse residential

with lots of vegetation

% of household heads men

% of heads up to 3 years of schooling

% of househoheads men

% of up to 3 resid

5,6%N=18

29%N=31

42%N=24

55%N=22

5,9%N=17

14%N=43

33%N=15

>10%10%

2%

>10%

>2%

10%

18,9% = Q2

>18,9% = Q2

40% >40%

31,6%

81% = Q3

>81% = Q3

81% = Q3

54,14% = Q3

Figure 3 Diagram of the best predictive model obtained from the leanodes with probability (P) and the number of CT (N). Teresina, Piau, Brazil.eads >64,2% = Q2soil coverage implicate in ecological and social processeswhich result in huge differences of magnitude on theincidence of the disease.Therefore, it is possible to infer that the process of

establishing and dissemination of VL in the urban envir-onment of Teresina, having a markedly heterogeneousspatial distribution, results from the socio-territorialorganization. Nevertheless, we believe that part of the

% of under 5 years

ld

Ratio between the total income of the last decile

/ total income of the poorest 40%

N=215

ents

Nominal mean income of head

% of household heads men

82%N=11

92%N=12

68%N=22

3.9%N=77

40%N=20

7,7%N=65

15%N=40

46%N=13

>2,49 = Q2

= Q3>31,6% = Q3

>81% = Q3

2,49 = Q2

>11,65% = Q2

11,65% =Q2

>453,50 = Q3453,50 = Q3

>77,13% = Q2

77,13% = Q2

>54,14% = Q3

rning sample (19931996). The frames in red correspond to terminal

grco

pe

(9

(6

(6

Almeida and Werneck International Journal of Health Geographics 2014, 13:13 Page 6 of 7http://www.ij-healthgeographics.com/content/13/1/13predictive deficiencies of the model is owing to thelack of a better definition of the territory as a spatial-social-environmental unit favorable to VL occurrence.The improvement on the predictive performance of area

at high risk for VL could be attained with the use of moreenvironmental indicators extracted from the satelliteimages of medium resolution [21,22] or with the use ofremote sensing images with high spatial resolution [23].The satellite images used in this study come from theLandsat Thematic Mapper 5 sensors, which have spatialresolution of 30 meters, in other words, it does not allowthe discrimination of elements on the earths surface forareas smaller than 900 m2. However, VL studies that usedmedium spatial resolution were able to identify some ele-ments related to vegetation coverage, soil use and patternsof urban occupation associated to the risk of the disease[12,22,24,25], but the difficulties and limitations of theseimages to properly characterize the local features thatdetermine the pattern of transmission of VL on the urbancontext are evident [22]. In future studies, high spatialresolution images should be used to better define the localenvironmental features related to VL occurrence [26].At last, considering the complexity of the factors involved

in the VL dissemination in the urban environment, theresults of this study demonstrate that the occurrence of VLon the outskirts of Teresina is intensely related to socioeco-nomic and environmental problems, arising from the urbanexpansion process and from the changes in the vector habi-

Table 3 Predictive performance of the Classification and Re(19931996) and validation sample (20012006) and their

Analysis ROC curve area Sensibility S

% (95% CI) % (95% CI) %

CART (learning sample)83 79

(7988) (7187)

CART (validation)66 52

(6170) (4560)

Teresina, Piau, Brazil.tat, due to the environmental imbalance caused by deforest-ation and land occupation with lack of adequate urbaninfrastructure [25]. In this perspective, focusing interven-tions in these considered high risk areas could be a usefulstrategy to improve the effectiveness of control measureswhile decreasing operational costs.

Additional file

Additional file 1: ROC curves figures for the validation and learningsamples.

Competing interestsThe authors declare that they have no competing interests.Authors contributionsASA participated in the articles conceptualization and conducted theliterature review, structured the database, analyzed and interpreted thecompiled data, and wrote the article. GLW participated in the articlesconceptualization and contributed to the analysis and interpretation of theresults and helped write the article. Both authors read and approved thefinal manuscript.

AcknowledgmentsThe research in this paper was funded by CNPq (160571/2011-1, 202088/2012-0 and 306267/2010-1).

Author details1Instituto de Medicina Social, Universidade do Estado do Rio de Janeiro, RuaSo Francisco Xavier, 524, Pavilho Joo Lyra Filho, 7 andar/blocos D e E,e 6 andar/bloco E, Maracan, CEP 20550-013 Rio de Janeiro, Brazil.2Instituto de Estudos em Sade Coletiva, Universidade Federal do Rio deJaneiro, Avenida Horcio Macedo, S/N - Prximo a Prefeitura Universitriada UFRJ, Ilha do Fundo - Cidade Universitria, CEP 21941-598 Rio deJaneiro, RJ, Brazil.

Received: 27 December 2013 Accepted: 18 March 2014Published: 20 May 2014

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doi:10.1186/1476-072X-13-13Cite this article as: Almeida and Werneck: Prediction of high-risk areasfor visceral leishmaniasis using socioeconomic indicators and remotesensing data. International Journal of Health Geographics 2014 13:13.Submit your manuscript at www.biomedcentral.com/submit

AbstractIntroductionMethodsArea of studyStudy designData and variablesResponse variablePredictor variables

Satellite image classificationData analysis

ResultsDiscussionAdditional fileCompeting interestsAuthors contributionsAcknowledgmentsAuthor detailsReferences

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