ABSTRACT - Universidade Federal de Goiás · no estudo 1245 casos, sendo que o ano de 2009, foi responsável por 37,02% dos casos. Identificou-se conglomerados de alto e baixo risco,

OriginalArticle

Rev.Eletr.Enf.[Internet].2015apr./jun.;17(2):223-37.Availablefrom:http://dx.doi.org/10.5216/ree.v17i2.27643.

Decisionmodelonthedemographicprofilefortuberculosiscontrolusingfuzzylogic

ModelodedecisãosobreoperfildemográficoparaocontroledatuberculoseusandológicaFuzzy

LaisaRibeirodeSá1,JordanadeAlmeidaNogueira2,RoneiMarcosdeMoraes31Nurse,MasterinModelsofDecisionandHealth.PhDStudentatthePostgraduatePrograminModelsofDecisionandHealth,UniversidadeFederaldaParaíba(UFPB).Joãopessoa,PB,Brazil.E-mail:[email protected],PhDinNursing.AssociateProfessoroftheHealthSciencesCenter,UFPB.JoãoPessoa,PB,Brazil.E-mail:[email protected],PhDinAppliedComputing.AssociateProfessoroftheDepartmentofStatistics,UFPB.JoãoPessoa,PB,Brazil.E-mail:[email protected].

ABSTRACT

Thisstudyaimedtodescribetherelationshipbetweendemographic factorsandthe involvementof tuberculosisby

applyingadecisionsupportmodelbasedonfuzzylogictoclassifytheregionsaspriorityandnon-priorityinthecityof

JoãoPessoa,stateofParaíba(PB).Asdatasource,weusedtheNotifiableDiseasesInformationSystembetween2009

and2011.Wechosethedescriptiveanalysis,relativerisk(RR),spatialdistributionandfuzzylogic.Thetotalof1,245

casesremainedinthestudy,accountingfor37.02%ofcasesin2009.Highandlowriskclusterswereidentified,andthe

RR was higher among men (8.47), with 12 clusters, and among those uneducated (11.65), with 13 clusters. To

demonstratethefunctionalityofthemodelwaselectedtheyearwithhighestnumberofcases,andthemunicipality

districtwithhighestpopulation.Themethodologyidentifiedpriorityareas,guidingmanagerstomakedecisionsthat

respectthelocalparticularities.

Descriptors:Tuberculosis;RelativeRisk(PublicHealth);SpatialAnalysis;FuzzyLogic;PublicHealthNursing.

RESUMO

Objetivou-sedescreverarelaçãoentreos fatoresdemográficoseoacometimentodatuberculose,aplicando-seum

modelodesuporteàdecisãobaseadoemlógicafuzzyparacategorizaçãodoslocaisemprioritáriosenãoprioritários,

nacidadedeJoãoPessoa-PB.Comofontededados,utilizou-seoSistemadeInformaçãodeAgravosdeNotificação,

entre2009e2011.Optou-seporanálisedescritiva,riscorelativo(RR),distribuiçãoespacialelógicafuzzy.Permaneceu

noestudo1245casos,sendoqueoanode2009,foiresponsávelpor37,02%doscasos.Identificou-seconglomerados

dealtoebaixorisco,sendoqueoRRfoimaiorentreoshomens(8,47),com12conglomerados,entreaquelessem

escolaridade(11,65),com13conglomerados.Parademonstrarafuncionalidadedomodelo,elegeu-seoanocommaior

númerodecasoseumbairrodomunicípiocommaiorcontingentepopulacional.Ametodologiapermitiuidentificar

áreasprioritáriasdirecionandogestoresparadecisõesquerespeitemasparticularidadeslocais.

Descritores:Tuberculose;RiscoRelativo;AnáliseEspacial;LógicaFuzzy;EnfermagememSaúdePública.

SáLR,NogueiraJA,MoraesRM.


224

INTRODUCTION

Tuberculosis (TB) isan infectiousdiseaseof chronic

evolution with sufficient diagnostic and therapeutic

resources for a proper and successful clinical

management. Despite the available technology, the

disease incidence is still high, with alarming numbers.

Estimates from the World Health Organization (WHO)

indicatethediseaseasthesecondleadingcauseofdeath

worldwide.TBwasthebasiccauseof1.4milliondeathsin

2011,and8.7millionpeoplefellillinthesameyear(1).

Althoughinrecentyears,Brazilhashownadeclinein

the number of cases, it still occupies the 19th position

among 22 countries, concentrating 80% of the

tuberculosisburdenintheworldandthe104thposition

relative to the incidence rate. In 2012 were reported

70,047newcases,correspondingtoanincidencerateof

36.1/100,000 inhabitants(1). Every year about 4,500

peoplediefromtuberculosis,therefore,thisisthefourth

leadingcauseofdeathfrominfectiousdiseasesandthe

leadingcauseofdeathamongAIDSpatients(2).

In 2012, in the state of Paraíba, located in

northeasternBrazil,werereported1,132newcasesofthe

disease, with an incidence rate of 29.7/100,000 in

habitants. The state capital, João Pessoa, presented an

incidencerateof46.4/100,000inhabitantsfornewcases.

Overthepast10years,thisindicatorhasshownafalling

trend,similartotheincidencerateinBrazil(2).

The studies agree that TB is conditioned to social

aspects, where poverty and poor sanitary conditions

determinants, together with the weaknesses of

management and organization of health services

collaborate to the increase and/or permanence of the

diseaseincidencerates(3–5).

Thus, new answers must be found to promote

fairness, ensuring access to patients, seeking not only

their care and well-being, but in a broader sense, the

consolidation of the Unified Health System (SUS) in its

guidelines.Thealternativeslieinthereadjustmentofthe

health care system in the assistance to these patients,

redefinition of procedures and organizational charts,

redefinition of institutional missions of civil society

entities, and the search for alternatives to solve the

problem. Among those alternatives, communication

shouldoccupyaprominentplace(6).

In thisperspective,andwiththesignificantnumber

ofTBcases,arosetheneedtoanalyzethedemographic

profileofthediseaseinthecityofJoãoPessoa(stateof

Paraíba – PB) to facilitate the decision-making process

related to thisdisease.As thevariablesassociatedwith

the involvement of tuberculosis have some degree of

uncertainty, a systembasedon fuzzy rulesbecomesan

interesting alternative for a decisionmodel that allows

thecategorizationofpriorityregionsusingeachvariable

separately.

Studies using the proposed model in various

morbidities were found in the literature, but only

assessingcasesintheirentirety(7-8).However,nostudies

associating cases of tuberculosis according to

demographic variables through a spatial analysis were

found,andthisisproposedinthepresentstudy.

Given the above, this study aimed to describe the

relationship between demographic factors and the

involvement of tuberculosis. It also applied a decision

supportmodelbasedonfuzzylogictoclassifytheregions

aspriorityandnon-priority,accordingtoeachvariable.

METHODS

Thisisanepidemiological,retrospectivestudy,based

onrecordsoftuberculosiscasesinthecityofJoãoPessoa.

It included the cases reported in the database of the

Notifiable Diseases Information System (Sinan) of the

Health Secretariat of the state of Paraíba between 1

January2009and31December2011.Thestudyexcluded

all cases inwhich data could lead to systematic errors,

such as cases from other municipalities without

informationaboutthehomedistrict.

Subsequently, was held a database refinement to

identifythevariablesofinterest:theyearofnotification,

gender,educational level,district (residenceunitof the

notified case and not necessarily where the infection



225

occurred). These data were arranged in a spreadsheet

and analyzed according to the frequency and

proportional distribution to generate the relative risk

(RR), spatial scan statistic (CSS) and fuzzy logic. From

thesevariables,tablesandgraphsweregeneratedfora

betterpresentationofresults.

TheBrazilianCensusconductedinBrazilintheyears

2000and2010wasusedtocalculatetheincidencerates.

The denominators were estimated from projections of

theresidentpopulationinthecity,usingthepopulation

baseof1,000inhabitantsineachdistrictofJoãoPessoa.

Therelativerisk(RR)wasrepresentedbytheratioof

the incidence rate in adistrict dividedby the incidence

rateoftheentireregion(JoãoPessoa).Theuseofmaps

to spatially represent the RR allowed primary analyzes,

suchas the identificationof regions thatpresented the

highest and lowest risks of disease occurrence. The RR

wasdividedintoclassintervals,previouslyestablishinga

color for each interval(9). Risk maps were prepared

accordingtotheyearofoccurrenceandvariablestudied

(total, gender and educational level). For the

interpretationoftheRRmap,thefollowingcriteriawere

defined(Table1):

Table1:Interpretationofclassesofrelativerisk*.RelativeRisk InterpretationofRelativeRisk

Greaterthanorequalto0andlessthan0.5

Districtswheretherelativeriskrangedfrom0to0.5,i.e.,relativerisklowerthanhalftheoverallriskofthestudyarea.

Greaterthanorequalto0.5andlessthan1.0

Districtswheretherelativeriskrangedfrom0.5to1.0,i.e.,higherthanhalftheoverallriskofthestudyarea.


Districtswheretherelativeriskrangedfrom1.0to1.5,sotherelativeriskisgreaterthantheoverallriskofthestudyareainlessthan50%.


Districtswheretherelativeriskrangedfrom1.5to2.0,sotherelativeriskishigherthantheoverallriskofthestudyareainmorethan50%.

Greaterthanorequalto2.0 Districtswheretherelativeriskwashigherthan2.0.*Adaptedtable(7-8,10).

Subsequently, the CSS method was used to detect

spatial clusters, allowing to outline the critical regions

using graphical computational algorithms. For spatial

analysis,weadopted theprobabilisticmodelofPoisson

thatassociates thearea informationwitha singlepoint

withinthepolygon,thecentroid,whichisthemasscenter

ofeachregionofthestudiedarea(11).

IntheCSSmethod,therestrictionisinrelationtothe

percentage of the population at risk, determining the

increaseofthesearchradiusuptoβ%ofthepopulation.

Thisstudyusedβ%ofthepopulationof1%,3%,5%and

10%. The maps from the respectiveβ% should be

comparedwiththeriskmapofeveryyeartofindthemap

thatbestfits.Thus,fromavisualinspectionofthemaps

representingthephenomenonunderstudy,wecompare

theCSSmapwith theriskmap,opting for theCSSmap

that most closely resembles the risk map. The spatial

clustersidentifiedwerepresentedonmapsinwhichthe

reddotsrepresentthecentroids(11).

After the spatial analysis, was applied the decision

model based on fuzzy logic. The fuzzy logic originates

fromtheconceptoffuzzysetsandrepresentsascheme

capable of translating inaccurate information into

numerical values, making it possible to solve problems

thatclassicallogiccouldnotsolve(12).Then,afuzzysetis

definedasaclassofobjectswithcontinuousdegreesof

association. This set is characterized by membership

functions assigned to each object of the set, varying

betweenzeroandone(13).

One of the most common ways to express this

knowledgeisthroughrulesofthecondition-actiontype.

Thus, all knowledge is represented by a set of rules in



226

which the conditions are established from a set of

linguistic terms, which are associated with the

input/outputvariables.Therefore,thefuzzyruleisaunit

capableofcapturingsomespecificknowledge(14).

Atthisstage,werecreatedtheinputvariables(total

relative risk, totalCSS, female relativerisk, female CSS,

male relative risk, male CSS, uneducated relative risk,

educated CSS, educated relative risk - at least literate,

educatedCSS-atleastliterate)andtheoutputvariables

(priorityandnon-priority)andthemembershipfunctions

foreachofthem.Eachofthesevariableshasitslinguistic

termsrepresentingafuzzyset(14).Inthefinalresult,itwas

possibleto identifythepriorityandnon-priorityregions

ofthecityofJoãoPessoa,withtheirrespectivedegrees

ofmembership.

TheethicalaspectsestablishedbyResolution196/96,

which regulates research on humans, were respected.

Sincethisisastudywithsearchinasecondarydatabase,

we used the Institutional Consent term issued by the

MunicipalSecretariatofHealthinJoãoPessoa-PBunder

number20.900/2013.

RESULTS

Intheperiodbetween2009and2011,werereported

1,329casesoftuberculosisinthecityofJoãoPessoa-PB.

Basedonthe inclusioncriteria,1,245casesremained in

thestudy,ofwhich461(37.02%)werereportedin2009,

417(33.49%)in2010and367(29.47%)in2011.Themale

genderwaspredominant(853/68.5%),aswellassubjects

withsomelevelofeducation(983/78.95%).

Toexemplify,intheanalysisofrelativeriskandCSS,

were used the maps of tuberculosis in João Pessoa in

2009, for the total, female, male, uneducated and

educated (Figure 1).Note thatmapswere built for the

entirestudyperiod.

The map of total RR showed the behavior of

tuberculosisinthedistrictsofJoãoPessoa-PB,wherethe

RRvariedbetween0and5.90per1,000inhabitants.The

spatialanalysisofcasesthroughstatisticalCSSidentified

clustersa thighand low risk.Nineteendistricts showed

spatial clusters for the year 2009, with 5%of the

population.ComparingthemapoftotalCSSwiththemap

of total risk, there is a coincidence of regionswith the

greatest RR ratios and those with significant spatial

clusters.

AnalyzingthemapsofriskandCSSbygender,theRR

washigher amongmen (8.47),with thepresenceof 12

clusters. The RR was lower among women, reaching a

maximum value of 4.66. However, the CSS method

identified 13 significant clusters. In the analysis of the

male population,was also observed that somedistricts

showed RR two or more times higher when compared

withthetotalRRandthefemalegender.

In relation to theeducational level, theRR reached

11.65amonguneducatedcasesand6.76amongeducated

individuals. In both groups were identified 13 clusters,

andinthefirst,therewasahigherconcentrationinthe

districtsofthecentralareaofthecityand/ordistrictswith

economicallyvulnerablepopulation.



227

Figure1:MapsofrelativeriskandCSSoftuberculosiscasesaccordingtoresidencedistrict.JoãoPessoa,PB,Brazil,2009.

Intheanalysisoffuzzyvariablesinvolvingrelativerisk

weredefinedfivelinguisticterms,namely:veryhighrisk,

highrisk,mediumrisk,verylowriskandlowrisk(Chart1).

The following membership functions of the fuzzy

variables were of trapezoidal type: total relative risk,

female relative risk, male relative risk, uneducated



228

relative risk, educated relative risk. The binary

membership function for theresultsof theCSSmethod

were named withScan and withoutScan. The following

membership functions of fuzzy variables were of

triangular type: total CSS, female CSS, male CSS,

uneducatedCSSandeducatedCSS.Theoutputvariable

obtainedpriorityandnon-priorityasalinguisticterm,and

membershipfunctionsoftrapezoidaltype(Chart1).

Chart1:MembershipfunctionsoftheinputvariablesofrelativeriskandCSS,andmembershipfunctionsoftheoutput

variable.JoãoPessoa,PB,Brazil,2009.

Fortheconstructionoftherulesdatabase,wereused

tenfuzzyvariablesobtainedthroughtheannualmapsof

therelativeriskandCSSmapsfortotal,female,maleand

uneducatedpopulation,aswellaseducatedpopulation.

Fivebankswerecreatedwithtenrulesdefinedforeach,

totaling 50 rules. Some of these rules are presented

belowforexemplification:

• IF (total relative risk is veryhigh)AND (total scan is

withScan),THEN(decisionispriority)

• IF (total relative risk is veryhigh)AND (total scan is

withoutScan),THEN(decisionisnon-priority);

After construction of the rules, was applied the

decisionmodelbasedonfuzzylogictoclassifythedistricts

aspriorityandnon-priorityfortuberculosisinrelationto

gender, educational level and total. This model was

appliedtoeachsetofrulesforall64districts,inallyears,

demonstrating its functionality. It canbeused as a low

cost method by managers and professionals, for an

effectivecombatofthemorbidityaccordingtothetarget

populationandtheregionsconsideredaspriority.



229

Toexemplifytheapplicationofthemodelbasedon

fuzzylogic,wechoseadistrictwithhighpopulationand

the year 2009, a periodwith the highest occurrence of

cases(Charts2-4).

Each column representsamembership function for

eachof thevariables intherule.Eachrowrepresentsa

rulethat isactivatedaccordingtothe inputdata,which

mayormaynotactivatethelastcolumn,accordingtothe

value of the input variables. Thus, the output function

shows the suggestion of decision to be made by the

manager for the district; as priority (partially or

completelyfilledbybluecolorontherightside)orasnon-

priority(partiallyorcompletelyfilledbybluecoloronthe

leftside).

Followingthesameconception,themodelconfirmed

the expected result, in which the elected district was

classified as priority in relation to the total population

(Chart2),malegender(Chart3)andeducated(Chart4).

Itwasclassifiedasnon-priorityinrelationtothefemale

gender(Chart3)anduneducated(Chart4).



230

Chart2:ResultofthetotallinguisticfuzzymodelinthedistrictofMangabeira-JoãoPessoa,PB,2009.



231

Chart3.A:ResultofthemaleandfemalelinguisticfuzzymodelinthedistrictofMangabeira-JoãoPessoa,PB,2009.



232

Chart3.B:ResultofthemaleandfemalelinguisticfuzzymodelinthedistrictofMangabeira-JoãoPessoa,PB,2009.



233

Chart4.A:ResultoftheuneducatedandeducatedlinguisticfuzzymodelinthedistrictofMangabeira-JoãoPessoa,PB,2009.



234

Chart4.B:ResultoftheuneducatedandeducatedlinguisticfuzzymodelinthedistrictofMangabeira-JoãoPessoa,PB,2009.



235

DISCUSSION

The disease concentration in the most populous

districtsorthoseoflowereconomicclassmaybedueto

itscharacterizationasasocialproblem,resultingfromthe

interactionofseveralfactors,amongwhichthelowfamily

income, poor education, poor/non-existent housing,

large families, high density communities, food

malnutrition, alcoholism, associated diseases,migration

flows,deficiencyofthehealthsystem(15)

.

Themajorityof studies foundcorroborate the local

scenebecause they characterize thedisease as a social

problemresultingfromtheinteractionofseveralfactors,

ofwhichthemostcitedwerethemalegender,lowfamily

income, poor education, poor/non-existent housing,

large families, alcoholism, smoking,mental illness,AIDS

andoccupationalrisk(4,16–20)

.

Thegenderasariskfactorcanbejustifiedbythefact

thathistoricallymenseekhealthservicesforpreventive

controlwithlessfrequency,especiallywhenitcomesto

basichealthcare,andgenderissuesareverystrongwhen

itcomestothehealth-diseaseprocess(21)

.

The actions related to care have always been

associatedwithwomen,andtheaccessofmentohealth

services is hindered by labor issues, when there is a

mismatch between the health services hours and the

workinghoursofmalesubjects.Asaresult,mentendto

searchforhealthservicesaftertheworseningofclinical

picture.Inadditiontobeseenasthehomeproviders,and

feeling more difficulty in expressing themselves, men’s

needsarenotmetinstantly.

In relation to educational level, the study results

showthatmostofthecasesreportedoccurineducated

people.However, theapplicationof thedecisionmodel

found thatmost districts were classified as priority for

uneducatedpeople,confirmingthisisalsoariskfactorfor

TB.Corroborating theabove in termsof education, the

longerthedurationofstudies,thelessertheoccurrence

of TB, showing that more years of study result in less

occurrenceofTB(22)

.

Thedecisionmodelbasedonfuzzylogicisconsidered

afundamentalmethodologicalsupporttorespondtothe

healthprocessuncertainties and the illnessof subjects.

Moreover, it improves the understanding of individual

riskandisessentialinepidemiologicalstudies.

The theory of fuzzy sets appears as a useful

methodological tool in social areas by shaping the

uncertainties inherent in its phenomena, especially in

epidemiology(23)

. Several epidemiological studies show

theimportanceofthetheoryoffuzzysetswhenstudying

the health and disease process, contributing in the

construction of diagnostics and expert systems. Among

thestudiesusingthetheoryoffuzzysetssatisfactorily,we

canhighlightafew:applicationoffuzzyclustersmodelfor

analysis and identification of different types of

tuberculosis(24)

;creationofa fuzzymodeltopredict the

resultofTBcaseswithongoingtreatment(25)

.

CONCLUSION

Theresultsshowedthatthedecisionmodelbasedon

fuzzylogicisalowcosttoolandallowedtheclassification

ofallthecitydistrictsaspriorityornon-priority,according

to each variable, enabling the visualization of

peculiarities, and guiding managers for decisions that

respecttheparticularitiesofeachlocation.Notethatthis

is an innovative approach because it visualizes the

totality, aswell as eachvariable separately, allowing to

know the vulnerability of each region for the effective

combatofthemorbidity.

Thus, a further understanding of the vulnerable

populations to the disease and the region become

possible,whichfacilitatesthedefinitionofpublicpolicies

forthepriorityandspecificareasandthepopulationof

thatregion.Also,policiesforthecombatandprevention

of morbidity can be defined together with health

professionals,makingitpossibletodecreasetheonsetof

tuberculosis.



236

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Received:12/11/2013.

Accepted:09/15/2014.

Published:06/30/2015.

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