Modelling the impact of MAUP on environmental drivers for Schistosoma japonicum prevalence

Abstract

Background: The modifiable areal unit problem (MAUP) arises when the support size of a spatial variable affects the relationship between prevalence and environmental risk factors. Its effect on Schistosomiasis modelling studies could lead to unreliable parameter estimates. The present research aims to quantify MAUP effects on environmental drivers of Schistosoma japonicum infection by (i) bringing all covariates to the same spatial support, (ii) estimating individual-level regression parameters at 30 m, 90 m, 250 m, 500 m, and 1 km spatial supports, and (iii) quantifying the differences between parameter estimates using five models.
Methods: We modelled the prevalence of Schistosoma japonicum using sub-provinces health outcome data and pixel-level environmental data. We estimated and compared regression coefficients from convolution models using Bayesian statistics.
Results: Increasing the spatial support to 500 m gradually increased the parameter estimates and their associated uncertainties. Abrupt changes in the parameter estimates occur at 1 km spatial support, resulting in loss of significance of almost all the covariates. No significant differences were found between the predicted values and their uncertainties from the five models. We provide suggestions to define an appropriate spatial data structure for modelling that gives more reliable parameter estimates and a clear relationship between risk factors and the disease.
Conclusions: Inclusion of quantified MAUP effects was important in this study on schistosomiasis. This will support helminth control programs by providing reliable parameter estimates at the same spatial support, and suggesting the use of an adequate spatial data structure, to generate reliable maps that could guide efficient mass drug administration campaigns.

Full text

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Preprint:Pleasenotethatthisarticlehasnotcompletedpeerreview.
ModellingtheimpactofMAUPonenvironmental
driversforSchistosomajaponicumprevalence
CURRENTSTATUS:ACC EPTED
AndreaAraujoNavas
UniversiteitTwenteFaculteitGeo-InformatieWetenschappenenAardobservatie
a.l.araujonavas@utwente.nlCorrespondingAuthor
FrankOsei
UniversiteitTwenteFaculteitGeo-InformatieWetenschappenenAardobservatie
RicardoJ.SoaresMagalhães
UniversityofQueensland
LydiaR.Leonardo
UniversityofthePhilippinesDiliman
AlfredStein
UniversiteitTwenteFaculteitGeo-InformatieWetenschappenenAardobservatie
DOI:
10.21203/rs.2.20917/v1
SUBJECTAREAS
Parasitology
KEYWORDS
Schistosomiasismodelling,modifiablearealunitproblem,uncertainty,Bayesian
statistics,convolutionmodel

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Abstract
Background:Themodifiablearealunitproblem(MAUP)ariseswhenthesupportsizeofaspatial
variableaffectstherelationshipbetweenprevalenceandenvironmentalriskfactors.Itseffecton
Schistosomiasismodellingstudiescouldleadtounreliableparameterestimates.Thepresentresearch
aimstoquantifyMAUPeffectsonenvironmentaldriversofSchistosomajaponicuminfectionby(i)
bringingallcovariatestothesamespatialsupport,(ii)estimatingindividual-levelregression
parametersat30m,90m,250m,500m,and1kmspatialsupports,and(iii)quantifyingthe
differencesbetweenparameterestimatesusingfivemodels.
Methods:WemodelledtheprevalenceofSchistosomajaponicumusingsub-provinceshealth
outcomedataandpixel-levelenvironmentaldata.Weestimatedandcomparedregression
coefficientsfromconvolutionmodelsusingBayesianstatistics.
Results:Increasingthespatialsupportto500mgraduallyincreasedtheparameterestimatesand
theirassociateduncertainties.Abruptchangesintheparameterestimatesoccurat1kmspatial
support,resultinginlossofsignificanceofalmostallthecovariates.Nosignificantdifferenceswere
foundbetweenthepredictedvaluesandtheiruncertaintiesfromthefivemodels.Weprovide
suggestionstodefineanappropriatespatialdatastructureformodellingthatgivesmorereliable
parameterestimatesandaclearrelationshipbetweenriskfactorsandthedisease.
Conclusions:InclusionofquantifiedMAUPeffectswasimportantinthisstudyonschistosomiasis.
Thiswillsupporthelminthcontrolprogramsbyprovidingreliableparameterestimatesatthesame
spatialsupport,andsuggestingtheuseofanadequatespatialdatastructure,togeneratereliable
mapsthatcouldguideefficientmassdrugadministrationcampaigns.
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Figures

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Figure1
StudyArea:TheMindanaoregioninThePhilippines.Bluedotsaretheaggregatedsurvey
dataatbarangay-level.

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Figure2
Environmentalriskfactorsextractionatpixel-levelfromcitieswithinbarangays

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Figure3
Posteriorestimatesandtheircredibleintervals:a)Normalizeddifferencevegetationindex;
b)Normalizeddifferencewaterindex;c)Landsurfacetemperaturedayd)Landsurface
temperaturenight;e)Elevation;f)Nearestdistancetowaterbodies.Abbreviations:SSA,
Spatialsupportofanalysis

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Figure4
Densityplotsfortheriskfactorsregressioncoefficients:a)Normalizeddifferencevegetation
index;b)Normalizeddifferencewaterindex;c)Landsurfacetemperaturedayd)Land
surfacetemperaturenight;e)Elevation;f)Nearestdistancetowaterbodies

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Figure5
Residualplotforthefiveincreasingspatialsupportsofanalysis.Thexaxisrepresentsthe
fittedprevalencevaluesforthefivespatialsupportsofanalysis.Theyaxisrepresentsthe
residualscalculatedbythedifferencebetweentheobservedandpredictedprevalence
values.

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Figure6
Proportionofsimulatedprevalencedatathatfittheobservedmaximumprevalencevalue.
a)SSA=30m,b)SSA=90m,c)SSA=250m,d)SSA=500m,e)SSA=1km.Abbreviations:SSA,
Spatialsupportofanalysis.

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Figure7
Proportionofsimulatedprevalencedatathatfittheobservedminimumprevalencevalue.a)
SSA=30m,b)SSA=90m,c)SSA=250m,d)SSA=500m,e)SSA=1km.Abbreviations:SSA,
Spatialsupportofanalysis.

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Figure8
Proportionofsimulatedprevalencedatathatfittheobservedmeanprevalencevalue.a)
SSA=30m,b)SSA=90m,c)SSA=250m,d)SSA=500m,e)SSA=1km.Abbreviations:SSA,
Spatialsupportofanalysis.
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