Person,PlaceandContext:TheInteractionbetweentheSocialandPhysicalEnvironmentonAdversePregnancyOutcomesinBritish

Columbiaby

AndersCarlEricksonB.Sc.,UniversityofVictoria,2004

M.Sc.,UniversityofNorthernBritishColumbia,2009

ADissertationSubmittedinPartialFulfillmentoftheRequirementsfortheDegreeof

DOCTOROFPHILOSOPHY

inInterdisciplinaryStudies

intheDivisionofMedicalSciences&DepartmentofGeography

AndersCarlErickson,2016UniversityofVictoria

Allrightsreserved.Thisthesismaynotbereproducedinwholeorinpart,by

photocopyorothermeans,withoutthepermissionoftheauthor.

ii

SupervisoryCommittee

Person,placeandContext:TheInteractionbetweentheSocialandPhysicalEnvironmentonAdversePregnancyOutcomesinBritishColumbia

by

AndersCarlEricksonB.Sc.,UniversityofVictoria,2004

M.Sc.,UniversityofNorthernBritishColumbia,2009

SupervisoryCommitteeDr.LauraT.Arbour,MDFRCPCFCCMG,DivisionofMedicalSciences,UniversityofVictoria&DepartmentofMedicalGenetics,UniversityofBritishColumbiaSupervisorDr.AleckOstry,PhD.,DepartmentofGeography,UniversityofVictoriaCo‐SupervisorLaurieH.M.Chan,PhD.,CenterforAdvancedResearchinEnvironmentalGenomics,UniversityofOttawaOutsideMember

iii

Abstract Thisstudywasapopulation‐basedretrospectivecohortofallsingletonbirthsin

BritishColumbiafortheyears2001to2006.Thepurposeofthisdissertationistoexamine

howsocialandphysicalenvironmentfactorsinfluencetheriskofadversepregnancy

outcomesandwhethertheyinteractwitheachotherorwithmaternalcharacteristicsto

modifydiseaserisk.Themainenvironmentalfactorsexaminedincludeambientparticulate

airpollution(PM2.5),neighbourhoodsocioeconomicstatus(SES),neighbourhoodimmigrant

density,neighbourhoodlevelofpost‐secondaryeducationlevelandtheurban‐ruralcontext.

Censusdisseminationareas(DAs)wereusedastheneighbourhoodspatialunit.Thedata

(N=242,472)wasextractedfromtheBCPerinatalDataRegistry(BCPDR)fromPerinatal

ServicesBC(PSBC).Themainperinataloutcomesinvestigatedincludebirthweightand

indicatorsoffetalgrowthrestrictionsuchassmall‐for‐gestationalage(SGA),termlowbirth

weight(tLBW),andintrauterinegrowthrestriction(IUGR),pretermbirth(PTB)and

gestationalage,gestationaldiabetesmellitus(GDM)andgestationalhypertension(GH).

Collectively,thisdissertationcontributestotheperinatalepidemiologicalliterature

linkingparticulateairpollutionandneighbourhoodSEScontexttoadversepregnancy

outcomes.AssumptionsaboutthelineareffectofPM2.5andsmokingonbirthweightare

challengedshowingthattheeffectsaremostpronouncedbetweenlowandaverage

exposuresandthatthemagnitudeoftheireffectismodifiedbyneighbourhoodand

individual‐levelcharacteristics.Theseresultssuggestthatfocusingexclusivelyonindividual

riskfactorsmayhavelimitedsuccessinimprovingoutcomeswithoutaddressingthe

contextualinfluencesattheneighbourhood‐level.Thisdissertationthereforealso

contributestothepublichealth,sociologicalandcommunity‐urbandevelopmentliterature

demonstratingthatcontextandplacematters.

iv

ExecutiveSummary Thisstudywasapopulation‐basedretrospectivecohortofallsingletonbirthsin

BritishColumbiafortheyears2001to2006.Thepurposeofthisdissertationistoexamine

howsocialandphysicalenvironmentfactorsinfluencetheriskofadversepregnancy

outcomesandwhethertheyinteractwitheachotherorwithmaternalcharacteristicsto

modifydiseaserisk.Themainenvironmentalfactorsexaminedincludeambientparticulate

airpollution(PM2.5),neighbourhoodsocioeconomicstatus(SES),neighbourhoodimmigrant

density,neighbourhoodlevelofpost‐secondaryeducationlevelandtheurban‐ruralcontext.

Censusdisseminationareas(DAs)wereusedastheneighbourhoodspatialunit.Thedata

(N=242,472)wasextractedfromtheBCPerinatalDataRegistry(BCPDR)fromPerinatal

ServicesBC(PSBC).Themainperinataloutcomesinvestigatedincludebirthweightand

indicatorsoffetalgrowthrestrictionsuchassmall‐for‐gestationalage(SGA),termlowbirth

weight(tLBW),andintrauterinegrowthrestriction(IUGR),pretermbirth(PTB)and

gestationalage,gestationaldiabetesmellitus(GDM)andgestationalhypertension(GH).

Thedissertationiscomprisedof7chapters.InChapter1,Ireviewtheshared

pathoetiologicaleffectsofparticulateairpollutionandthesocialenvironmentin

contributingtoadversepregnancyoutcomes,includingtheroleofoxidativestress,

inflammationandendocrinemodificationonfetal‐placentaldevelopment.Chapter2

providesabackgrounddiscussiononthemethodsanddatausedthroughoutthe

dissertation.Thisincludesthehierarchicalnatureofthesocialenvironmentandhow

multilevelandspatialstatisticalmethodscanbeusedtoexploreenvironmentalhealth

relationships.Thisleadsintothefourresearchchapters(Chapters3to6)whichshowthe

existenceoftheserelationshipsinanobservationalepidemiologicalsetting.Finally,Chapter

7providestheoverallconclusionsandrecommendations.Additionalmaps,resultsand

backgroundinformationareprovidedasappendices.Researchethicsboardapprovalwas

grantedbytheUniversityofVictoria(ethicsprotocol#:11‐043)andbytheUniversityof

Alberta(studyid:Pro00028662)withfundingprovidedinpartbytheCanadianInstituteof

HealthResearch(CIHR)OperationalGrant(protocol#:200903‐202069).

Inthefirstresearchpaper(Chapter3),Iassessthequantityofcigarettessmoked

duringpregnancyandthemagnitudeofadversepregnancyoutcomesfollowedbytesting

theassociationbetweenthequantityofcigarettessmokedwithotherSESandbehavioural

riskfactorsthatalsoinfluencepregnancyoutcomes.Theresultsshowasignificantdose‐

responseincreaseinriskforSGA,tLBWandIUGR,andindicatethatself‐reportsofheavy

v

smoking(≥10cigarettes/day)earlyinpregnancywereassociatedwithnothavinghigh

schooleducation:adjustedOR(95%CI)=3.80(3.41‐4.25);beingasingleparent:2.27

(2.14‐2.42);indicationdrugoralcoholuse:7.65(6.99‐8.39)and2.20(1.88‐2.59)

respectively,andattendingfewerthan4prenatalcarevisits:1.39(1.23‐1.58),andtobe

multiparous:1.59(1.51‐1.68)comparedtolight,moderateandnon‐smokerscombined.

Theseresultssuggestthatheavysmokinginpregnancycouldbeusedasamarkerfor

lifestyleriskfactorsthatincombinationwithsmokinginfluencebirthoutcomes.Iusethe

numberofcigarettesandthisheavysmokingsub‐populationinthefollowingtwopapersas

apotentialhighriskgrouppossiblymoresusceptibletoPM2.5exposure.

ThepurposeofChapter4wastodeterminetherelationshipbetweenPM2.5exposure

andcontinuousbirthweight,andtotestthepotentialmodificationbymaternalriskfactors

andindicatorsofsocioeconomicstatus.Theresultsshowanon‐linearnegativeassociation

ofPM2.5andbirthweightandthatthisrelationshipismodifiedbytheneighbourhood

contextandmaternalcharacteristics.Usingrandomcoefficientmodels,thereisevidence

thatneighbourhood‐levelSESvariablesandPM2.5havebothindependentandinteracting

associationswithbirthweightwhichtogetheraccountfor49%ofthebetween‐

neighbourhooddifferencesinbirthweight.Thissuggeststhatcertainsub‐populationsmay

bemoreorlessvulnerabletoevenrelativelylowdosesPM2.5exposure.Iprovidefurther

analysisoftheassociationbetweenPM2.5andtheotherDA‐levelvariablesonmeasureof

birthweight,includingtLBW,SGA,IUGRaswellasPTBinAppendix3.Theresultsshowfind

consistentdose‐responseassociationsbetweenPM2.5exposureandthemeasuresof

impairedfetalgrowth,butnoassociationwithPTB.

InChapter5Ifocusontheinteractionbetweenmaternalsmokingandwhether

neighbourhoodfactorscaneitherpotentiateorbufferitsnegativeeffectonbirthweight.

SimilartoPM2.5,asignificantnegativeandnon‐linearassociationwasfoundbetween

maternalsmokingandbirthweightwhichwashighlyvariablebetweenneighbourhoods

andshowedevidenceofeffectmodificationwithneighbourhood‐levelfactors.HighDA‐level

SEShadastrongpositiveassociationwithbirthweightbuttheeffectwasmoderatedwith

increasedcigarettes/day.Conversely,heavysmokersshowedthelargestincreasesinbirth

weightwithrisingneighbourhoodeducationlevels.IncreasedlevelsofPM2.5andimmigrant

densitywerenegativelyassociatedwithbirthweight,butshowedpositiveinteractionswith

increasedlevelsofsmoking.Oldermaternalageandsuspecteddrugoralcoholusebothhad

vi

negativeinteractionswithincreasedlevelsofmaternalsmoking.Theresultssuggestthat

thesocialandenvironmentalcontextmattersinhowsmokingcanaffectbirthweight.

InChapter6IassesstheassociationofPM2.5andSES‐relatedneighbourhoodfactors

ontheriskofgestationalhypertension(GH)andgestationaldiabetesmellitus(GDM).The

resultsshowaconsistentdose‐responseassociationintheriskofGHandGDMwith

increasinglevelsofPM2.5.HigherDA‐levelSESandeducationwereassociatedwithlower

risksforbothGHandGDM,whilehigherimmigrantdensityandhigherDA‐meanBMI

showedanincreasedrisk.GDMshowedconsiderableeffectheterogeneityinurbanareas

wheretheinteractionbetweenPM2.5andSESgreatlymodifiedtheriskofGDM.

Furthermore,theseassociationsarepotentiallymorepronouncedamongmotherswith

largerpre‐pregnancyBMI.TheinclusionoftheDA‐levelSESandPM2.5variablesreduceda

substantialproportionofthebetween‐DAvariabilityintheriskofGHandGDM;however,

thewassignificantremainingunexplainedrandominterceptvariancewhichwasshownto

be,atleastpartially,spatiallyclusteredatalocalscale.

Collectively,thisdissertationcontributestotheperinatalepidemiologicalliterature

linkingparticulateairpollutionandneighbourhoodSEScontexttoadversebirthoutcomes.

AssumptionsaboutthelineareffectofPM2.5andsmokingonbirthweightarechallenged

showingthattheeffectsaremostpronouncedbetweenlowandaverageexposuresandthat

themagnitudeoftheireffectismodifiedbyneighbourhoodandindividual‐level

characteristics.Theseresultssuggestthatfocusingexclusivelyonindividualriskfactors

mayhavelimitedsuccessinimprovingoutcomeswithoutaddressingthecontextual

influencesattheneighbourhood‐level.Thisdissertationthereforealsocontributestothe

publichealth,sociologicalandcommunity‐urbandevelopmentliteraturedemonstrating

thatcontextandplacematters.

vii

TableofContentsSupervisoryCommittee........................................................................................................................iiAbstract.....................................................................................................................................................iiiExecutiveSummary...............................................................................................................................ivTableofContents...................................................................................................................................viiListofTables.............................................................................................................................................xListofFigures..........................................................................................................................................xiListofAbbreviations............................................................................................................................xiiAcknowledgments................................................................................................................................xivDedication................................................................................................................................................xvChapter1:TheSharedPathoetiologicalEffectsofParticulateAirPollutionandtheSocialEnvironmentonFetal‐PlacentalDevelopment...............................................................1Abstract..................................................................................................................................................................11.0Introduction..................................................................................................................................................12.0Person,PlaceandContext:ThePlacental,PhysicalandSocialEnvironments..................32.1ThePlacenta.............................................................................................................................................32.2ThePhysicalEnvironment:ParticulateAirPollution.............................................................42.3TheSocialEnvironment:Socio‐economicStatus,Diet,Smoking&AllostaticLoad...7

3.0BiologicalMechanismsLeadingtoAdversePregnancyOutcomes........................................93.1OxidativeStress......................................................................................................................................93.2InflammationandImmunologicAlterations............................................................................103.3.MechanismsofOxidativeStressandInflammationInvolvedinAdversePerinatalOutcomes........................................................................................................................................................12

4.0ThePhysicalandSocialEnvironmentandtheirRelationtoAdversePerinatalOutcomes.............................................................................................................................................................154.1.PM‐inducedoxidativestressandinflammatorymechanisms.........................................154.2MaternalDietandMicronutrientIntake....................................................................................174.3MaternalSmokingandEnvironmentalTobaccoSmoke(ETS)Exposure....................204.4AllostaticStressandGlucocorticoidExposure........................................................................21

5.0Discussion....................................................................................................................................................226.0Conclusion....................................................................................................................................................247.0Endnotes.......................................................................................................................................................248.0References....................................................................................................................................................30

Chapter2:MethodologicalBackground.......................................................................................511.0Introduction................................................................................................................................................512.0Socio‐economicStatusBackground..................................................................................................522.1MeasuringSocio‐economicStatus................................................................................................522.2Contextual(population‐level)MeasuresofSES......................................................................54

3.0MultilevelModels&Analysis...............................................................................................................563.1RandomInterceptModel..................................................................................................................583.2RandomSlopeModel..........................................................................................................................603.3InclusionofLevel‐2VariablesandCross‐LevelInteractions............................................613.4MultilevelLogisticRegression........................................................................................................63

4.0SpatialDependence:Mechanisms,MethodsandModels.........................................................644.1SpatialDependence&SpatialHeterogeneity...........................................................................654.2SpatialWeightMatrices....................................................................................................................654.3SpatialRegressionModels...............................................................................................................684.4RateSmoothing.....................................................................................................................................69

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5.0PerinatalDataRegistriesandAdversePregnancyOutcomes................................................705.1TheBCPerinatalDatabaseRegistry.............................................................................................705.2AdversePregnancyOutcomes........................................................................................................71

6.0ExposureAssessment..............................................................................................................................746.1ExposureAssessmentTerminology............................................................................................746.2ExposureAssessmentQuantification..........................................................................................74

7.0References....................................................................................................................................................76Chapter3:Heavysmokingduringpregnancyasamarkerforotherriskfactorsofadversebirthoutcomes:apopulation‐basedstudyinBritishColumbia,Canada........86Abstract................................................................................................................................................................861.0Background..................................................................................................................................................872.0DataandMethods.....................................................................................................................................883.0Results...........................................................................................................................................................904.0Discussion....................................................................................................................................................955.0Conclusion.................................................................................................................................................1006.0References.................................................................................................................................................100

Chapter4:Thereductionofbirthweightbyfineparticulatematteranditsmodificationbymaternalandneighbourhood‐levelfactors:amultilevelanalysisinBritishColumbia,Canada................................................................................................................104Abstract.............................................................................................................................................................1041.0Background...............................................................................................................................................1052.0DataandMethods..................................................................................................................................1063.0Results........................................................................................................................................................1104.0Discussion.................................................................................................................................................1185.0Conclusions...............................................................................................................................................1226.0EndnoteforChapter4..........................................................................................................................1227.0References.................................................................................................................................................124

Chapter5:Airpollution,neighbourhoodandmaternal‐levelfactorsmodifytheeffectofsmokingonbirthweight:amultilevelanalysisinBritishColumbia,Canada.........130Abstract.............................................................................................................................................................1301.0Background...............................................................................................................................................1312.0DataandMethods..................................................................................................................................1323.0Results........................................................................................................................................................1354.0Discussion.................................................................................................................................................1425.0Conclusions...............................................................................................................................................1466.0References.................................................................................................................................................146

Chapter6:AssociationofGestationalDiabetesandHypertensionwithincreasedfineparticulatematterandneighbourhood‐levelsocioeconomicfactors:amultilevelanalysisinBritishColumbia,Canada..........................................................................................1511.0Background...............................................................................................................................................1522.0DataandMethods..................................................................................................................................1533.0Results........................................................................................................................................................1564.0Discussion.................................................................................................................................................1685.0Conclusions...............................................................................................................................................1716.0References.................................................................................................................................................171

Chapter7:Conclusions.....................................................................................................................1771.0Introduction.............................................................................................................................................1772.0SummaryofResearchandContributions....................................................................................1772.1.HeavysmokingasamarkerforunmeasuredSES‐relatedlifestyleriskfactors....177

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2.2Effectmodificationofmaternalsmokingonbirthweightbyneighbourhood‐levelfactors...........................................................................................................................................................1782.3Epidemiologicalfindings...............................................................................................................180

3.0Limitations................................................................................................................................................1813.1UseofDAsasneighbourhoods....................................................................................................1813.2ThePM2.5Land‐useRegressionModel.....................................................................................1823.3MissingData........................................................................................................................................1833.4FirstNationsBirths..........................................................................................................................184

4.0OverallImplicationsandFutureConsiderations......................................................................1845.0References.................................................................................................................................................186

Appendix1RiskSurfaceMaps......................................................................................................190Appendix2AdditionalFiguresforChapter4..........................................................................203Appendix3ResultsforSGA‐3,SGA‐10,IUGR,TermLBW,andPTB.................................206 

x

ListofTablesTable1:Biologicalfactorsinvolvedinpregnancy,theirroleandup/down‐regulation..........29Table2:MaternalCharacteristicsbySmokingStatusinBC,2001‐2006.......................................92Table3:OddsRatiosofCovariateRiskFactorsPredictingLevelofMaternalSmokingin

B.C.2001–2006(n=163,867)......................................................................................................95Table4:Descriptivestatistics#forindividual(Level‐1)andDA(Level‐2)covariates.........111Table5:AdjustedindividualandDA‐levelfixedeffectsoncontinuousbirthweight............112Table6:AdjustedindividualandDA‐levelfixedeffectsoncontinuousandtermbirth

weightandtheirmodificationbyPM2.5(Model‐4)..............................................................114Table7:Randomeffectsandmodeldiagnosticsfromhierarchicallinearmodelsfor

continuousbirthweightinBC,Canada....................................................................................117Table8:AdjustedindividualandDA‐levelfixedeffectsoncontinuousandtermbirth

weightandtheirmodificationbyPM2.5(Model‐5)..............................................................124Table9:Descriptivestatistics#forindividual(Level‐1)andDA(Level‐2)covariateson

termbirthweight..............................................................................................................................136Table10:Adjustedfixedeffectsforlevel‐1andlevel‐2covariatesoncontinuousterm

birthweight.........................................................................................................................................137Table11:AdjustedindividualandDA‐levelfixedeffectsoncontinuousbirthweightand

theirmodificationbymaternalsmoking(Model3)...........................................................138Table12:RandomEffectsandModelDiagnostics................................................................................140Table13:Summaryofpopulationandneighbourhoodcharacteristics,[n(%)].....................158Table14:ORsforGHandGDMinrelationtoPM2.5andDA‐levelSESvariables......................159Table15:ORsforgestationaldiabetesinrelationtoPM2.5,SES,andRuralResidence.........161Table16:ORsforgestationaldiabetesinrelationtoPM2.5,SES,andmaternalBMIª............162Table17:Randomeffectsandmodeldiagnosticsfromhierarchicallogisticmodelsfor

GHinBC,Canada...............................................................................................................................164Table18:Randomeffectsandmodeldiagnosticsfromhierarchicallogisticmodelsfor

GDMinBC,Canada...........................................................................................................................164 

xi

ListofFiguresFigure1:Aconceptualframeworkofthesharedmechanismsofsocio‐economic

determinantsandparticulateairpollutionexposurecontributingtoadversepregnancyoutcomes...........................................................................................................................9

Figure2:Proposedpathwayscontributingtoadversepregnancyoutcomes..............................12Figure3:Proposedpathwaysofparticulateairpollutioncontributingtooxidativestress

andinflammationleadingtoadversepregnancyoutcomes............................................16Figure4:Proposedpathwaysofhowthesocialenvironmentinteractstoproduceexcess

systemicandplacentaloxidativestressandinflammationleadingtoadversepregnancyoutcomes.........................................................................................................................20

Figure5:Schemeofplacentalcirculationandfeatures(Grey’sAnatomylithographs)...........26Figure6:Invasiondefectsinpreeclampsia...............................................................................................26Figure7:Sixtheoreticalmultilevelproposition.......................................................................................58Figure8:nbynbinaryspatialweightmatrixW.......................................................................................66Figure9:Moran’sIscatterplotsshowingthedegreeofclusteringofmodelresidualsusing

differentKNNspatialweightmatrices......................................................................................67Figure10:Moran’sIclustermapshowstheclusteringofmodelresidualsbetweenthe

arealunits(DAs)usinga6‐KNNspatialweightmatrix.....................................................68Figure11:DistributionofMaternalDailyCigaretteConsumptioninBC,2001‐06...................90Figure12:AdjustedOddRatiosofAdverseBirthOutcomesandLevelsofMaternal

Smoking.................................................................................................................................................94Figure13:AdjustedPredictedEffectsofPM2.5onBirthWeight.....................................................112Figure14:AdjustedPredictedEffectsofmaternalriskfactorsonbirthweightacross

levelsofPM2.5....................................................................................................................................115Figure15:AdjustedPredictedEffectsofDA‐levelfactorsonBirthWeightacrosslevels

ofPM2.5.................................................................................................................................................116Figure16:ResultsfromModel‐5includingaSES*Ruralinteraction............................................123Figure17:AdjustedPredictedEffectsofMaternalSmokingonBirthWeight..........................137Figure18:AdjustedPredictedEffectsofMaternalSmokingonBirthWeightacross

DA‐levelFactors..............................................................................................................................138Figure19:AdjustedPredictedEffectsofMaternalSmokingonBirthWeightacross

Maternal‐levelFactors..................................................................................................................139Figure20:Neighbourhood‐specificslopesofmaternalsmokingonbirthweight..................141Figure21:DirectedAcyclicGraphsdepictingthehypothesisedrelationshipsforGHand

GDM......................................................................................................................................................157Figure22:AdjustedORsand95%CIsforGDMandGHinrelationtoPM2.5quintiles

andDA‐levelSESvariables..........................................................................................................160Figure23:PredictedProbabilityofGestationalDiabetesMellituswith95%CIsin

relationtoPM2.5,SESandRuralResidence..........................................................................161Figure24:PredictedProbabilityofGestationalDiabetesMellituswith95%CIsinrelationto

PM2.5,SESandBMI..........................................................................................................................163Figure25:ClustersandOutliersofLocalizedSpatialAutocorrelationinDA‐level(random

intercept)ResidualsforGestationalhypertensioninB.C.,2001–2006.................166Figure26:ClustersandOutliersofLocalizedSpatialAutocorrelationinDA‐level(random

intercept)ResidualsforGestationalDiabetesMellitusinB.C.,2001–2006.........167 

xii

ListofAbbreviations

Acronym Definition

11β‐HSD2 11β‐hydroxysteroiddehydrogenasetype2AhR ArylHydrocarbonReceptorACTH AdrenocorticotropicHormoneAPO AdversePregnancy/PerinatalOutcomeBC BritishColumbiaBCPDR BritishColumbiaPerinatalDatabaseRegistryBMI BodyMassIndexCI ConfidenceIntervalCd CadmiumCOX‐2 Cyclo‐oxygenase‐2CO,CO2 CarbonMonoxide,CarbonDioxideCRA CumulativeRiskAssessmentCRH CorticotropinReleasingHormoneCRP C‐reactiveproteinCSD CensusSubdivisionCVD CardiovascularDiseaseCYP CytochromeP450DA DisseminationAreaDPP DefectiveDeepPlacentationETS EnvironmentalTobaccoSmokeEVT ExtravilliousTrophoblastFGR FetalGrowthRestrictionGDM GestationalDiabetesMellitusGH GestationalHypertensionGPx GlutathionePeroxidaseGST Glutathione‐S‐TransferaseHHC HyperhomocysteimiaHPAaxis Hypothalamus‐Pituitary‐AdrenalaxisICC Intra‐classcorrelationIUGR Intra‐UterineGrowthRestrictionI/Rinjury Ischemic‐reperfusioninjuryLBW LowBirthWeightLDL LowDensityLipoproteinsLHA LocalHealthAreaLUR LandUseRegressionMOR MedianOddsRatioMI MultipleImputation

xiii

NO,NO2 NitrogenOxide/DioxideOR OddsRatioPAH PolycyclicAromaticHydrocarbonsPAP ParticulateAirPollutionPCV ProportionalChangeinVariancePIH/PE PregnancyInducedHypertension/PreeclampsiaPM ParticulateMatterPM2.5 ParticulateMatterlessthan2.5micronsindiameterpPROM (premature)PrelabourRuptureofMembranesPSBC PerinatalServicesBritishColumbiaPTB PretermBirthRI RandomInterceptROS ReactiveOxygenSpeciesRS RandomSlopesEng solubleendoglinsFlt solublefms‐liketyrosinekinaseSES Socio‐economicStatusSESi Socio‐economicStatusIndexSMR StandardizedMorbidityRateSNP SingleNucleotidePolymorphismSOD SuperoxideDismutasetLBW TermLowBirthWeightUFP UltrafineParticlesuNK(cells) uterinenaturalkiller

xiv

Acknowledgments

Firstandforemost,Iwouldliketoacknowledgemysupervisorandmentor

overthepastseveralyearsDr.LauraArbour.Iwouldn’tbeheretodaywithouther

support,guidance,patience,andencouragement.Herhumilityandwhole‐hearted

approachtotheimportantworkthatshedoesinacademe,clinicalandcommunity

settingsisinspirational.Second,I’dliketothankDr.LaurieChanforhissupport

overthepastdecadedatingbacktowhenIstartedmyMastersin2006.His

mentorshipoverthattimeatUNBCtaughtmemanylessonsonhowtobeagraduate

studentandacademicinvolvedincommunity‐levelresearch.I’dliketothankDr.

AleckOstryforhison‐pointadvice,insight,andtherightdoseofinvolvementwhen

needed.AbigthankyoutoformercommitteememberDr.EleanorSettonformany

things,frombringingmeintoCAREXtoputtingmynameforwardforvarious

opportunities,Eleanorhasbeenanenduringsupporterandcheerleader.I’dliketo

acknowledgeDr.ScottVennersasafriend,supporter,andco‐applicantonaCIHR

grantthatfundedthemajorityofmyPhD.I’dalsoliketoacknowledgeandthankDr.

AdrienBarnettforhismentorshipoverthepasttwoyearsonseveralstatistical

issues.Hisfastandclearemailresponseswereinstrumentalinmovingmyresearch

forward.ThankyoutocolleaguesSarah,Beatrixe,SorshaandSirishaforalltheir

helpandfriendlyfacesoverthesemanyyears,aswellastoDr.PerryHystadforthe

useofhisairpollutiondata,adviceandfriendship.I’dliketoacknowledgethestaff

atPerinatalServicesBCfortheirsupportregardingdataaccessandmanuscript

review.Finally,wordscannotdescribetheunconditionalsupport,friendshipand

lovemylifepartner,bestfriendandwifeKeeleyNixonprovidedthroughoutthis

journey.Whetherkillingmewithkindnessorjustgivingmespacetowallowinmy

owndespair,sheisalwaystherenourishingmyheart,soulandstomach.Hercopy‐

editinginthefinalpushtogetthisdissertationinshapeandofftomycommittee

wasinvaluable,Iloveyousomuch.

xv

Dedication

Iwouldliketodedicatethisdissertationtothreepeoplewhoinspiredand

encouragedmetopursuethestudyofmedicalandhealthgeography.Theyinclude,

Dr.DeniseCloutier,Dr.PatrickMcLeodandthelateDr.HarryFoster.Iwashiredby

Deniseasaco‐opstudentinthesummerof2002toworkforPatrick,amedical

geneticistwithakeeninterestindiseasemapping,intheMedicalGeneticsClinicat

VictoriaGeneralHospital.Patrick’spluckyenthusiasmandabilitytocommunicate

complexdiseasemechanismsandDenise’scompassionateandfeministteachingsof

thesocialdeterminantsofhealthgotmehookedonmedicalsciencesand

epidemiology.Iwouldcontinuetoworkandvolunteerpart‐timeforPatrickforthe

nexttwoyears,enrollinginthegeographyhonoursprogramwithhimandDeniseas

myco‐supervisors.Itwasduringthistransitiontohealthgeographyandenrollment

intothehonourprogramthatIwasintroducedtoDr.Foster.Hispassionforthe

underlyingenvironmentalcausationsofvariousdiseases,mostnotablyselenium

andothermicronutrientdeficiencies,sparkedmyinteresttheenvironmentallinks

tohealthanddisease.Coincidently,itwaswhileworkingforPatrickthatImetmy

currentsupervisorDr.LauraArbourwhomanyyearslaterintroducedmetoDr.

LaurieChanwhichbroughtmebackintoacademeformyMastersatUNBCand

initiatedthiswholejourney.Everythinggoesfull‐circle,andsothisiswhyIdedicate

thisworkofstudytothosethreeindividuals.

Chapter1:TheSharedPathoetiologicalEffectsofParticulateAirPollutionandtheSocialEnvironmentonFetal‐Placental

DevelopmentEricksonA.C.,ArbourL.Thesharedpathoetiologicaleffectsofparticulateairpollutionandthesocialenvironmentonfetal‐placentaldevelopment.JournalofEnvironmentalandPublicHealth.2014.Published.

AbstractExposuretoparticulateairpollutionandsocioeconomicriskfactorsareshowntobe

independentlyassociatedwithadversepregnancyoutcomes;however,theirconfounding

relationshipisanepidemiologicalchallengethatrequiresunderstandingoftheirshared

etiologicpathwaysaffectingfetal‐placentaldevelopment.Thepurposeofthispaperisto

exploretheetiologicalmechanismsassociatedwithexposuretoparticulateairpollutionin

contributingtoadversepregnancyoutcomesandhowthesemechanismsintersectwith

thoserelatedtosocioeconomicstatus.Herewereviewtheroleofoxidativestress,

inflammationandendocrinemodificationinthepathoeitologyofdeficientdeep

placentationanddetailhowthephysicalandsocialenvironmentscanactaloneand

collectivelytomediatetheestablishedpathologylinkedtoaspectrumofadversepregnancy

outcomes.Wereviewtheexperimentalandepidemiologicalliteratureshowingthat

diet/nutrition,smokingandpsychosocialstresssharesimilarpathwayswiththatof

particulateairpollutionexposuretopotentiallyexasperatethenegativeeffectsofeither

insultalone.Therefore,socially‐patternedriskfactorsoftentreatedasnuisanceparameters

shouldbeexploredaspotentialeffectmodifiersthatmayoperateatmultiplelevelsofsocial

geography.Thedegreetowhichdeleteriousexposurescanbeamelioratedorexacerbated

viacommunity‐levelsocialandenvironmentalcharacteristicsneedsfurtherexploration.

1.0IntroductionOverthelastdecade,chronicexposuretoambientairpollutionhasbecome

increasinglyrecognizedasanimportantriskfactorunderlyingadversepregnancyoutcomes

(APOs)[1–9].Inparallel,theassociationsbetweensocio‐economicstatus(SES)andAPOs

areamongthemostrobustfindingsinperinatalresearch[10–12],whichpersistevenin

settingswithuniversalaccesstohealthcare[13–16].Whileinterestintheintersection

betweenhealthandthesocialenvironmentislongstanding[17–19],renewedattentionhas

beenpropelledbytwoindependentprogressionsinquantitativeresearch.Thefirstisthe

popularizationofmultilevelstatisticalmodelsandtheabilitytoseparatetheindividual‐

leveleffectsfromthoseoftheirencompassingsocialandphysicalenvironments[20–26].

2

Thesecondistheemergingresearchonthebiologicaleffectsofpsychosocialstresson

healthanditsmodificationbyenvironmentalfactors.Thereisnowmountingevidencethat

stresscaninteractwithchemicalexposurestoexacerbatethetoxiceffectandthe

physiologicalresponsetoagreaterextentthaneitherinsult(stressorchemical)acting

alone[27–31].Furthermore,theaccumulationoflow‐levelexposurestomultiplechemicals

viamultiplesourcesandpathwaysshowevidenceofdoseadditionandsynergism[32–34].

Forexample,synergismwasobservedbetweenaqueouscigarettetarandotherrespirable

particles(e.g.asbestosfibers,particulatematter,dieselexhaust)[35].Recognitionofthese

interactionshavebeenincorporatedintoseveralconceptualmodelsandstudydesignsof

cumulativeriskofchemicalandnon‐chemicalexposures[36–39]withmodelsrecently

developedtoidentifythesepotentiallydouble‐exposedpopulations[40,41].Two

complimentaryreviewsofthesemodelshavebeenrecentlypublished[42,43].

AlthoughthecausesofAPOsaremultifactorial,theplacentaplaysthemain

intermediaryrolebetweenthemother’sphysicalandsocialenvironmentandthefetus,[44–

50].Importantly,aperturbedintrauterineenvironmentinhibitingthefetalgrowth

trajectorymayalsohavelong‐termadulthealthimplicationsassuggestedbythe

developmentaloriginsofdiseasehypothesis[51–53].Thereforeeffortstounderstandthe

underlyingmechanismsofthephysicalandsocialenvironmentthatcontributetothe

disproportionateriskofAPOsacrossthesocio‐economicspectrumisrequiredinorderto

targetpreventativeandrestorativeinterventions.Thisreviewwillexaminehowtheshared

pathoetiologicaleffectsofexposuretoparticulateairpollutionandSESactonthefetal‐

placentalunitleadingtoadversepregnancyoutcomes.Thiswillbeaccomplishedby

buildingonconceptualpathwaymodelsofairpollutionandSESetiologicmechanismson

APOs[54,55].Wereviewtheroleoftheplacentainthiscontext,describingitsphysiology

andobstetricalpathologiesfollowedbyadescriptionofparticulateairpollution,andits

toxicokineticsinrelationtoplacentationandhowitcanleadtoAPOs.Wehighlightspecific

indicatorsofSESandtheirbiologicalpathwaysthatintersectwithairpollutionexposure

andhowthismaycontributetoincreasedsusceptibilityforAPOs.Potentialimplicationsand

interventionsaresummarizedintheconclusion.Ouraimisforthisreviewtobearesource

forresearchersinterestedinenvironmental‐perinatalepidemiology.Understandinghow

correlatedsocialandenvironmentalexposuresattimesoverlaptoproducepotential

synergisticandmodifiableeffectswillhelpguidefutureresearchandintervention

strategieswiththeaimtoimprovetheoverallhealthofthepopulation[36–40].

3

2.0Person,PlaceandContext:ThePlacental,PhysicalandSocialEnvironments

2.1ThePlacenta

Themammalianplacentaismultifunctionalandvitaltofetaldevelopment.Formed

fromtwogeneticallydistinctorganisms,itismultifunctionalandvitaltofetaldevelopment

yetsituatedoutsidethefetalbodywithalimitedlifespan.Notablecharacteristicsuniqueto

humansandtheGreatApesincludedeepinterstitialimplantationandahighlyinvasive

hemochorialphenotypethusallowingthedirectinteractionofmaternalbloodandfetal

chorionictissues[56].Interestingly,thisparticularaspectofplacentalevolutionhaslessto

dowithnutrienttransferefficiencythanpreviouslythoughtandmorelikelyimplicatesthe

highlyregulatedmaternal‐fetalimmunologicalrelationship[57–59].

Thefirsttrimesterisacriticalperiodinpregnancyinvolvingimplantationandinitial

placentation,twoeventshighlysusceptibletodisturbance(seeendnote1).The“Great

ObstetricalSyndromes”[60]suchasearly/recurrentmiscarriage,pregnancyinduced

hypertensionandpreeclampsia(PIH/PE),fetalgrowthrestriction(FGR),placental

abruption,pre‐labourruptureofthefetalmembranes(PROM)andspontaneouspreterm

labourmaysharecommonetiologicalmechanismsarisingfromdefectivedeepplacentation

(DDP)[61,62].Together,theseconditionsmaycomplicatebetween17to29%ofall

pregnancies[63],andareforthepurposeofthisreviewreferredtocollectivelyasAPOs.

Furthermore,theseconditionsmayleadtoepigeneticprogrammingofadultdisease

susceptibilityincludingobesity,diabetes,cardiovascularandreproductivediseases,allwith

theirownsubstantialsocietalcosts[52,64–66].DDPreferstotheshallowinvasionofthe

placentalbedintothematernaldeciduaandmyometriumincludingincompleteremodeling

oftheuterinespiralarteries[62,67].Thelatterisavitaleventduringwhichundernormal

conditionstheendothelialliningofthespiralarterywallsareremodeledtoaccommodate

theinundationofmaternalbloodflowstartinginthesecondtrimester[68].Spiralarteries

thatfailtoundergothisvascularremodelingarenotonlynarrowerindiameter,butalso

remainexcessivelyresponsivetovasoconstrictivecompoundssuchasstresshormones(see

endnote2).Theetiologicaltrigger(s)leadingtoDDParethoughttoinvolveeitherearly

placentaloxidativestresswhichtriggersaninflammatoryresponse,orvise‐versa,an

atypicalinflammatorymaternalimmuneresponsetothefetal‐placentalunitleadingto

placentaloxidativestressandfurtherinflammation[69,70].Thedifferencebetweena

normalandanaffectedpregnancyisamatterofdegreesonacontinuumwithindividual

4

biologicalandbehaviouralvariabilitynestedwithinthesocialandphysicalenvironment

[12,24–26,68,69,71–73].

2.2ThePhysicalEnvironment:ParticulateAirPollution

Airpollutionisageneraltermusedtodescribethepresenceofagents(particulates,

biologicals,chemicals)inoutdoororindoorairthatnegativelyimpacthumanhealth.

SeveralcommonairpollutantshavebeenassociatedwithAPOs,includingcarbonmonoxide

(CO),nitrogendioxide(NO2),sulfurdioxide(SO2),ozone,particulatematter(PM)and

polycyclicaromatichydrocarbons(PAHs)[1];however,attentionhasfocusedonthelatter

twocompoundsshowingstrongmolecularevidenceofcytotoxicity,mutagenicity,DNA

damage,oxidativestressandinflammation[55,74–79].WhiletheobservedrisksofAPOsin

relationtoairpollutiontendtobemodest,thepopulationattributableriskcanbequite

largeduetothepervasivenessofexposuretothegeneralpopulation[9].Significantrisks

havebeenobservedeveninsettingswithrelativelylowambientairpollutionexposure

[80,81].Therefore,asmallincreaseinriskcanhavealargepublichealthimpact.PTBand

FGRaremajorriskfactorsofperinatalmortalityandseriousinfantmorbiditiescontributing

toincreasedhealthcareandsocietalcosts[82–87].

Particulatematter(PM)isacomplexmixtureofvaryingchemicalandphysical

properties.Itisdefinedaccordingtoparticlesizeintotheinhalablecoarsefraction(PM10,

2.5‐10μm),thefinerespirablefraction(PM2.5,≤2.5μm)andtheultrafinefraction(UFP,≤

0.1μm).Theirubiquityandrecognizedhumanhealthriskshavedeemedthemastoxic

[88,89].CharacterizingPMbyparticlesizeisimportantforseveralreasons.First,particle

sizedictatesthelocationofdepositionintherespiratorysystem[88,90].Second,particle

sizecangivesomeindicationofitsgeneralsourceandbehaviour.Forexample,PM10is

mainlyderivedfrommechanicalprocessessuchaswindblownsoil,pollen,mineralsand

dustfromroads,farmsandindustrialoperations.PM10tendstogravitationallysettlesina

matterofhourstodays.Conversely,PM2.5isaprimaryby‐productofcombustionand

atmosphericreactionswithprecursorgasessuchasSO2,nitrogenoxides,ammoniaand

volatileorganiccompounds(VOCs).PM2.5canremainsuspendedinairfordaystoweeks,

andareconsequentlymorepronetolong‐rangetransport.Precipitationaccountsfor80‐

90%ofPM2.5removalfromtheatmosphere[88].Third,thechemicalcompositionis

markedlydifferentbetweenPM10andPM2.5mixtures.DerivedmainlyfromtheEarth’scrust,

PM10typicallycontainsoxidesofiron,calcium,silicon,andaluminum;whereasPM2.5

mixturesderivedfromanthropogeniccombustionsourcesaremainlycomposedof

5

sulphates,nitrates,ammonium,tracemetals,elementalcarbonandorganichydrocarbons

(e.g.PAHs)[88].ChemicaldifferencesandrelativeproportionsalsodifferwithinthePM10

andPM2.5mixtureswithregional(urban‐to‐rural)andinter‐urban(urban‐to‐urban)

differencesaswellasintra‐urbanspatialvariation[88,91–93].Thereforetrimesterand

demographicdifferencesinresidentialmobilityandintra‐urbanpopulationdifferencesare

importantstudydesignissuestoconsider[94,95].Finally,PM10,PM2.5andUFPsdifferby

theirtoxicologicalmechanisms,suchastheiroxidativepotential,whichmayreflecttheir

differencesinsize,surfaceareaand/ortheirchemicalconstituentcompositions,although

theytendtobecorrelated[76,92,96,97].Transitionmetalssuchascopper,nickel,lead,

chromium,iron,vanadiumandcobaltamongothermetalsarevariablypresentinambient

airabsorbedtoPM2.5[92,93].Theirdirectoxidativeactionorredoxpotentialtocreate

reactiveoxidativespecies(ROS)isonepossiblemechanismastohowPMinducesoxidative

DNAandproteindamage[78,97].

ThereisaccumulatingevidencethatsuggestsUFPsmaybethefractionofPM

responsibleformanyoftheadversehealtheffectsreportedinairpollutionstudies

[78,79,97,98].UFPsareasmallproportionbymassbutmakeupalargeproportionin

particlenumberandhavegoneeitherunmeasuredormisclassifiedasPM2.5[88,98].Their

smallsizefacilitatesbettertissuepenetrationdeepintolungalveoliandintoepithelialcells

restrictingtheirclearanceviamacrophagephagocytosis[98].Animalstudieshaveshown

thatUFPscantranslocateacrossthelungepitheliumintobloodcirculationandaccumulate

inotherorgans,includingtheliver,spleen,kidneys,heart,brainandreproductiveorgans

[98].ThehighsurfaceareaofUFPsfavourstheabsorptionofPAHsandpossiblytransition

metalswhichhasshowntolocalizeinthemitochondriainducingmajorstructuraldamage.

ThiscouldbeapossibleexplanationtoUFP’sexhibitedhigheroxidativepotentialcompared

tolargerPMfractionsofthesamematerial[79].Recentattentionhasbeengiventopro‐

inflammatoryandendocrine‐disruptingpropertiesofdieselemissions,amajorsourceof

UFPsinambientair[31,99–101].

Polycyclicaromatichydrocarbons(PAHs)areorganicsubstancesthatconstitutea

classofover100individualchemicalcompoundsmadeupofcarbonandhydrogenatoms

formedintorings[102].WhiletoxicologicaldataexistforindividualPAHs(benzo[a]pyrene

beingthemostcommonlyusedPAHindicator),theyalmostalwaysoccurascomplex

mixtures(e.g.soot,tobaccosmoke,creosote,dieselexhaust)[103].Thusitisdifficult,and

arguablyfutile,toassessthetoxicityofindividualPAHcomponentsonlytobecompounded

6

bythelikelihoodofinteractions[75,104,105].Combustionoforganicmatterandfossilfuels

arethemainsourceofatmosphericPAHswiththeirdistributionandmagnitude

concentratedalongtransportationcorridors(roadandrail)andland‐useareaswithheavy

industrialactivities.However,mainstreamandenvironmentaltobaccosmoke(ETS)remain

aleadingsourceofPAHexposure[106].PAHsaregenerallynon‐volatile(i.e.stable)and

havelowwatersolubility.Asaconsequence,PAHsoftenbindtoPM2.5andUFPinthe

atmosphere.Residencytimesintheatmospheredependonweatherconditions,PAH

molecularweightandtheemissionsource(e.g.stackvs.tailpipe)withatmospheric

depositionasthemainsourceofPAHstosoil,vegetationandsurfacewater.Onceinaquatic

systems,PAHsareoftenfoundabsorbedtosuspendedparticlesorboundtosediments

settledonthebottomwheretheypersistorareslowlybiodegradedbymicroorganisms.

WhilePAHscanbioaccumulateinsomeaquaticandterrestrialorganisms,theytendtonot

biomagnifyinfoodsystemsduetotheirmetabolisminhigherorderspecies[102,106].

However,itistheinefficientclearanceandactionofthehighlyreactivePAHmetabolites

thataresuspectedtocausecytotoxicity,mutagenicity,DNAdamage,oxidativestressand

tumourgenesis[75,106].

MuchoftheworkelucidatingthemechanismsinwhichPMandPAHselicitadverse

cellulareffectshavebeenconductedusingcardiovasculardisease(CVD)andlungcanceras

models[76–78,97,107–109].AlthoughseeminglydifferentdiseasesfromAPOs,thereare

severalsimilaritiesbetweenthem.First,bothAPOsandCVDrelatedoutcomesare

associatedwithPMexposurelevelswhichvarybySES[40,110,111],butarealsoassociated

withothersociallypatternedriskfactorssuchassmoking,poororinadequatediet,

psychosocialstress,obesityanddiabetes[12,112–114].CVDandAPOsalsosharemany

otherriskfactorssuchasthepresenceofsystemicinflammationandpre‐existing

hypertension.Interestingly,PIH/PEisariskfactorformaternalCVDlaterinlifeandalsoin

theoffspringifaffectedbyIUGR[115–117].CVDanddisordersofDDPhavesimilarly

affectedcellulartissuesintheirrespectivetargetsystems(i.e.endothelialcellsofthe

cardiovascularsystemandinthehighlyvascularisedplacenta)whichareparticularly

susceptibletooxidativeandinflammatoryinjury[97,118].Highplasmahomocysteine

concentrationsarepositivelyassociatedwithvasculopathyandinfarctionintheplacental‐

uterineandcoronarysystemsincreasingtheriskofspontaneousPTBandCVDevents

respectively[119,120].Fittingly,highdensitylipoproteincholesterolmaybeprotective

againstspontaneousPTBandCVDevents[120,121].Finally,PMandPAH‐induced

7

mutagenicity,cytotoxicity,DNAdamageandoxidativestresslinkedtolungcancerhavealso

beenobservedinthefetal‐placentalunit[122,123],andexposureearlyinpregnancymay

contributetotheriskofcongenitalanomaliesandearly(sub‐clinical)pregnancyloss[124–

127].

2.3TheSocialEnvironment:Socio‐economicStatus,Diet,Smoking&AllostaticLoad

Thesocialenvironmentplaysasignificantroleinmaternalandperinatalhealthwith

indicatorsoflowsocio‐economicstatus(SES)consistentlyamongthestrongestpredictors

ofadversepregnancyoutcomes[10–12].ThecausalpathwaysinwhichSEScontributesto

APOsandillhealthingeneralcanbeconceptualizedintermsof‘downstream’ormediating

exposures,stressesandbehavioursactingontheindividualthrough‘upstream’society‐

leveldeterminantssuchaspoverty,pooreducation,incomeinequalityandsocial

discrimination/marginalizationoverthelifespan[12].IndicatorsoflowSESassociatedwith

PTBandFGRincludematernalanthropometry(pre‐pregnancyBMI,height,gestational

weightgain),nutritionandmicronutrientstatus,cigaretteuse,genitaltractinfectionsand

inflammation,cocaineandotherdruguse,physicallydemandingwork,quantityandquality

ofprenatalcare,andpsychosocialfactorsincludinganxiety,depressionandstress(e.g.lack

ofsocial,familial,andmaritalsupport,povertyorfinancialhardship,physical/verbalabuse,

neighbourhoodcrime)[12,24,26,54].Forthepurposeofthisreview,thefocusherewillbe

onthreethatengagewiththeoxidativestressandinflammationpathwaystopotentially

interactwithexposuretoparticulateairpollution.Theyinclude:1)adiet‐micronutrient

pathway[55,128–131],2)cigarettesmokeexposure[35,132–135],and3)allostatic

activationoftheHPA‐axisandcorrespondingglucocorticoidproduction[47,72,136–138].

Nutritionanddietcaninfluenceperinatalhealthinopposingdirections.Poor/under‐

nutritionsuchashighfat/caloriedensefoodandlowmicronutrientintakeismore

prevalentamongwomenfromlowSESbackgroundswhichmaypartlyexplainhigherrates

ofsomeAPOs[12,139–142].Conversely,adequatedietandmicronutrientstatusprovides

resilienceagainstoxidativestressandinflammationcausedbyvariousexposuresincluding

airpollution,allostaticstress,infectionorsmoking[55,118,128,129,131,143].Maternal

exposuretomainstreamorenvironmentalcigarettesmokeduringpregnancyisassociated

withnumerousAPOsincludingcongenitalanomalies[127,144–146].Theirexposure

prevalenceisassociatedwithindicatorsoflowSESaswellasothersocially‐patternedrisk

factors[147–149],andremainsoneofthemostmodifiableriskfactorswithpotentialfor

beneficialintervention.OtherriskfactorsassociatedwithlowSESsuchasobesity,

8

(gestational)diabetesandhypertension[13,113,150]alsoengagetheoxidativestressand

inflammatorypathwaysandcouldthereforealsopotentiallyinteractwithPMexposureto

increasesusceptibilitytoadverseeffectsasevidencedinstudiesofcardiovascularhealth

[114,151,152].Recentstudieshaveobservedincreasedrisksofpreeclampsiaand

gestationaldiabetesassociatedwithmeasuresofairpollution[153–156]withonestudy

showingpositiveeffectmodificationbypre‐existingandgestationaldiabetes[154].

EvidenceshowsthatchroniclifestressorsassociatedwithlowSESatmultiplelevelsof

organization(individual,household,community)resultinacumulativebiologicaltollonthe

bodyaffectingmultiplesystemsandincreasingsusceptibilitytonumerousailments

[21,157–160]includingAPOs[15,26,161,162].

Theconceptofallostasisandallostaticload/overloadhasbeenproposedtodescribe

theindividualstressresponsetoaneventasanecessaryandadaptiveprocessthereby

removingtheimplicitnegativeconnotationattachedtotheterm‘stress’[163].Stresscanbe

positiveortolerablewhenitimprovesfunctionandperformanceandmayhavelong‐term

adaptivebenefits.However,thismaydependonavailablecopingresourcessuchasone’s

psychologicalresistance,resilienceandabilitytorecover.Negativeortoxicstressoccurs

whenrealorperceivedenvironmental/socialdemands,ortheanticipationofsuch,become

tooextremeorunpredictabletherebyexceedingone’s(perceived)abilitytocope(e.g.no

senseofcontrol,adversechildhoodexperiencesandotherformsoftrauma)[164,165].

Therefore,allostasisisthemultisystembiologicalresponsethatpromotesadaptationusing

systemmediatorssuchascortisol,(nor)epinephrine,vasopressin,renin,andglucagon

[165,166].Whereasallostaticloadandoverloadisthecumulativetoll(wearandtear)on

biologicalsystemsafterprolongedorpoorlyregulated(hyper/hypoactivated)allostatic

responses.Forexample,thecardiovascularsystemisextremelysensitivetostressinterms

ofincreasedbloodpressure;however,metabolicdisorderssuchasdiabetesandobesityas

wellasimmunefunctionimpairmentarealsolinkedtochronicstress.Furthermore,lifestyle

copingmechanismsasaresponsetochronicstresshavetheabilitytoeitherbufferor

exasperatetheeffect(e.g.exercise,diet,sleep,socialinteractionsorlackthereof)[163].

Thereforeinlightoftheabove,itisourbeliefthatthefetal‐placentalunitisthesitewhere

thephysicalandsocialenvironmentsconvergeandinteracttoinfluencereproductivehealth

whichwedescribefurtherbelow.

Figure1illustratestheinter‐connectednessbetweenparticulateairpollution

(PM/PAH)andSESonhowtheymayactdiscretelyorinacombinedmannertoyieldAPOs.

9

UsingFigure1asaguide,thefollowingtextwillreviewthetwomajormechanisms

(oxidativestressandinflammation)throughwhichthephysicalandsocialenvironments

arebelievedtonegativelyaffectthefetal‐placentalunitandhowtheymaycombine/interact

toleadtothemultifactorialnatureofAPOs.

Figure1:Aconceptualframeworkofthesharedmechanismsofsocio‐economicdeterminantsandparticulateairpollutionexposurecontributingtoadversepregnancyoutcomesThephysicalenvironment(orange)consistingofparticulateairpollutionandthesocialenvironment(green)consistingofcommunityandindividual‐levelsocialfactors/stressorsconvergetoaffectthefetal‐placentalenvironment(blue)viaoxidativestressandinflammatorymechanismspotentiallyleadingtoadversepregnancyoutcomes.

3.0BiologicalMechanismsLeadingtoAdversePregnancyOutcomes

3.1OxidativeStress

Aptlyknownas“TheOxygenParadox”,oxygenisbothessentialandtoxictothe

multicellularaerobicorganismswhoseveryevolutionwasdependentonleveragingthis

anaerobicwasteby‐productintoahigherenergyproducingadvantage[167].Observedin

allmammals,asteepoxygentensiongradientfrom20%inouratmosphereto3‐4%oxygen

concentrationinmostinternaltissuesistheprimarydefenseagainstoxidativedamage.

Secondaryandtertiarylayersofprotectionincludeantioxidantdefensesaswellasdamage

removal,repairandapoptoticresponsesystems[168,169].Thesegeneticallyadaptive

responsesareupregulatedinthepresenceofreactiveoxygenspecies(ROS)generatedas

naturalby‐productsofcellularaerobicmetabolismandexposuretovarioustoxins.

Oxidativestressoccurswhenthereisanimbalancebetweenpro‐andantioxidantcapacity.

Forexample,superoxideisthemostcommonintracellularROSinmammals.Itis

producedbythemitochondriaasametabolicby‐productbutalsofromthemetabolismof

10

variousgrowthfactors,drugsandtoxinsbyoxidizingenzymessuchasNADPH‐oxidaseand

cytochromeP450(CYP450).Superoxideisreducedbysuperoxidedismutase(SOD)into

hydrogenperoxide(H2O2)whichisthenfurtherreducedintowaterbyglutathione

peroxidase(GPx)andcatalase.UndernormalphysiologicalconditionsH2O2actsas

intracellularsecondarymessengers;however,it’saccumulationalongwithsuperoxidecan

reactwithfreeironionsornitricoxidetoformhighlytoxichydroxyl(OH∙)orperoxynitrite

(ONOO‐)ionsrespectively[70,168].FreeironisacommonmetalfoundabsorbedtoPM,and

theantioxidanthemeoxygenase‐1(HO‐1)facilitatesitsconjugationandremovalthrough

theincreasedavailabilityofferritintherebypreventingtheformationofreactivehydroxyl

molecules[92,170–172].DeficienciesinHO‐1havebeenassociatedwithseveralAPOssuch

asrecurrentmiscarriage,FGRandpreeclampsia[171,172].

Commonantioxidantsincludeenzymatic(e.g.SOD,GPx,catalase,HO‐1)andnon‐

enzymaticcompounds(e.g.vitaminCandE,glutathione,β‐carotene,ubiquinone)[118].

Geneticpolymorphismsand/ormicronutrientdeficienciesinantioxidantenzymes

precursorscanimpairantioxidantcapacity,whilechronicexposuretotoxicants,

psychosocialstress,bacteria,virusesandotherinducersofinflammationcanfosterpro‐

oxidantburden[70,77,118,172].Oxidativestressisunavoidable;however,underoptimal

conditionsthepresenceofROSleadstohomeostaticadaptationandaresafelyremoved.

Failuretoeffectivelymanageoxidativestresscanresultinalteredcellularfunctionas

excessROSdegradelipids,proteinsandDNApotentiallyinitiatingpathologicalprocesses.

Referto[168]foranextensivereviewontheroleofcellularROSinpregnancyoutcomes.

3.2InflammationandImmunologicAlterations

Itiswellrecognizedthatthematernalimmunesystemplaysacentralrolethroughout

theentirepregnancy,frompre‐implantationtoparturition,andisinfluencedbythe

inflammatoryresponseofthemothertoherenvironmentaswellastoherpartner(see

endnote3).Alternativetopreviouslyhypothesized[173],thematernalimmunesystemis

notpassiveorsuppressedduringimplantationanddevelopmentofthesemi‐allogeneic

placentaandfetus.Rather,itexertsexecutiveinfluenceontheestablishmentand

progressionofthepregnancyasanimmune‐mediatedqualitycontrolmechanismto

maximizematernalandoffspringhealth[44,173].Thisisachievedbyfavouringpro‐oranti‐

inflammatoryenvironmentsatdifferenttimesduringpregnancyfordifferentpurposes.For

instance,implantation,initialplacentationandparturitionarecharacterizedbyapro‐

inflammatoryenvironmentwhereasananti‐inflammatorystateprevailsformostofmid‐

11

gestation[174].Thefavouredlocalizedimmunologicalresponsehoweverishighlymodified

bytheinfectious,inflammatory,stress,nutritionalandmetabolicstatusoftheindividual

andthuscanbeinfluencedbyenvironmentalagentssuchasPM[175–177]and/oravailable

coping,socialandnutritionalresources[44,128,164,178].Therefore,inflammationis

believedtobeonepathwayinvolvedinbothPMandSES‐mediatedAPOs.

Chronicandacuteinflammationisacomplexresponseprocessmediatedbyarealor

perceivedattackfromforeignsubstances.Theinnateimmuneresponseistherapid

automaticresponsetoexternallyoriginating(exogenous)substancessuchaspathogens,but

alsofrominternal(endogenous)dangersignalsincludingproductsoftrauma,ischemia,

necrosisoroxidativestress[179].Theresponseincludesthereleaseofpro‐inflammatory

signalingcytokineproteinssuchasinterleukinsIL‐1β,IL‐6andtumournecrosisfactor

(TNF‐α)whichservetorecruitneutrophilstoaffectedtissues.However,therecruited

neutrophilsreleaseROSandhydrolyticpro‐inflammatoryenzymes(induciblenitricoxide

synthase(iNOS),cyclooxygenase(COX‐2)andprostaglandins(PG‐E2))whichdisturb

normalcellsinadditiontoaffectedtissueswhichinturnleadstoincreasedROSand

oxidativestress[180,181].Theplacentaisamulti‐functionalorgananditsroleatthe

maternal‐fetalinterfaceasthemainproducerofendocrinesteroidandproteinhormonesas

wellastheimmunologicbarrierbetweenmotherandfetuspositivelyinteractforthe

successofthepregnancy[44,173].Thisisachievedthroughanon‐linearseriesofpositive

andnegativefeedbackpathwayswiththestimulationorsuppressionofmoleculeswithpro‐

andanti‐immunosuppressantproperties(interleukins,galectins,placentalgrowthfactor,

andhumanchorionicgonadotropin(hCG))[182–184].Theproductionofthesecytokines,

chemokinesandotherimmune‐regulatoryagentsmediatethecoordination,migrationand

functionofseveralmaternalimmunecells(e.g.uterinenaturalkillercells(uNK))that

participateinearlypregnancyeventssuchasendometrialreceptivityofembryo

implantation,tissueremodeling,immunetoleranceandvascularadaptationtoinvading

placentaltrophoblastcells[44,182–184].Interferenceoraberrantproduction/secretionof

thesesubstancesbyvariousstressorsincludinginfection,toxinsandthoseactingthrough

theHPA‐axismayresultintheimpairedmaternalimmuneresponseleadingtothehallmark

DDPsyndromecomplicationsdescribedabove(earlypregnancyloss,PIH/PE,PROM,FGR,

prematurelabour,Figure2)[44,61,69,134,175,185–187].

12

3.3.MechanismsofOxidativeStressandInflammationInvolvedinAdversePerinatalOutcomes

3.3.1Impairedfertilityand(recurrent)miscarriage

Duetoimmortaltimebias,miscarriageisnoteasilymeasuredinpopulationorcohort

studieswithoutcarefuldesignmethodologies[188,189];however,associationsbetween

infertilityandairpollutionhavebeenmade[190,191].Oxidativestresshasshowntohavea

directeffectonfertilityandembryodevelopment.Forexample,obesemiceshowed

increasedROSsynthesisandoxidationinoocyteswithareducedabilityofzygotesto

developtotheblastocyststageprovidingevidencethatimpairedcellularantioxidant

capacitycanlimitsuccessfulovulationandfertilization[118].Dividingmitoticcellsare

particularlysensitivetooxidativedamageandareshowntoenteratransientgrowth‐

arrestedstateasaprotectivemechanismuntilthestresshaspassed.Thus,severeor

chronicoxidativestressmayhampercelldivisionorcausecellularnecrosisreducingor

terminatingembryoviability[72,169].Alternatively,anexaggeratedinflammatorystatevia

aviral,toxicand/orallostaticloadcouldleadtomaternalimmunemaladaptationto

Figure2:ProposedpathwayscontributingtoadversepregnancyoutcomesTheco‐presenceofmaternalandpaternalbiologicalfactorscanresultinprotectionorincreasedsusceptibilitytotheinteractionwiththephysicalandsocialenvironments.Cumulativenegativeexposuresearlyinpregnancyresultinginexcessoxidativestressandinflammationmaycauseacascadeofeventsleadingtodefectivedeepplacentation.Dependingonthedegreeofseverity,thereducedtransplacentalperfusioncanresultinvariouspathologiesassociatedwitharangeofobstetriccomplicationsandoutcomes[60,61,69,70].

13

conceptionleadingtorestrictedtrophoblaststemcellaccumulationintheearlyperi‐

implantationembryoresponsiblefortheproductionofhormonesthatenablessuccessful

implantation(Figure2)[44,72].

Oxidativestressisimplicatedinfirsttrimestermiscarriagefromprematureplacental

perfusionofmaternaloxygenatedbloodandaccompanyingROSintotheearlyembryonic

environment[192].Earlyembryodevelopmentoccursinalowoxygenstate,anditisnot

untilthetenthtotwelfthweekofgestationthatmaternalbloodbeginstograduallyinfiltrate

theintervilliousspaceoftheyetfullydevelopedplacenta.Thelimitedoxygenenvironment

isthoughttoactasaprotectivemechanismagainstthedeleteriousandteratogeniceffects

ofROSonearlystemcellsatatimeofextensivecelldivision[64,138].Thisearlyhypoxic

environmentalsoplaysavitalphysiologicalroleinplacentalcelltypedifferentiation

switchingfromproliferativevillouscytotrophoblastsintoinvasiveextravillioustrophoblast

(EVT)importantinspiralarteryremodeling[193].Attheendofthefirsttrimester,oxygen

tensionrisessharplywhichcoincideswiththeinfusionofoxygenatedmaternalbloodinto

theplacentaandtriggersanapoptoticcascadethatservestoestablishthedefinitivediscoid

placenta.However,in70%ofearlymiscarriagecasesEVTinvasionisinsufficientallowing

fortheprematureonsetofmaternalintraplacentalcirculationanditsconsequentialburstof

ROSontheconceptus[70,192].Severecasesmayresultinpregnancyfailurewhilemore

modestcasesmayinitiatefetal‐maternaladaptiontoimpairedspiralarteryremodeling

leadingtothepathologyforfurthercomplicationslaterinpregnancysuchasPIH/PE

(Figure2)[69,70,193].

3.3.2Pregnancyinducedhypertension,preeclampsia,andprelabourruptureofmembranes

Whileoxidativestressandinflammationareconditionsofnormalpregnancy,theyare

consistentlyelevatedincasesofPIH/PEandbotharecentralinitspathology.PIH/PEstems

fromadefectinearlytrophoblastinvasioninsufficienttofullyconvertthespiralarteries

intolow‐resistancechannels[68,194].Theretentionofsmoothmusclecellsremainsactive

tocirculatingvasoconstrictingagentssuchasstresshormones(e.g.glucocorticoids)and

otherstimulants.Thediminished,butmoreimportantly,theintermittentperfusionof

maternalbloodintotheintravilliousspaceproducestransienthypoxiaresultinginachronic

ischaemia‐reperfusion(I/R)typeinjury.ThisfurtherprovokesROSsynthesisandexcess

sheddingofplacentalmicrovesicleswhichhavepro‐inflammatory,anti‐angiogenicand

procoagulantactivityinitiatingendothelialdysfunction[68–70].Elevatedcirculatinglevels

ofplacentaldebrisandROSbiomarkersintheplacentaltissuesofpreeclampticwomenare

14

welldocumented[68,179,194].Similarly,PROMcanbeconsideredpartoftheDDP

syndromebutmayrepresentaphenotyperesultingfromalesssevereDDP

pathophysiologycomparedtopreeclampsia[61,62].Excessoxidativestressarisingfrom

multiplecauses(infection,inflammation,smoking,cocaineuse)havebeenimplicatedin

PROMinadditiontoitsroleinDDP[70].BothPIH/PEandPROMareleadingcausesof

pretermbirthwhilePIH/PEisamajorriskfactorforFGR(Figure2)[69].Deficienciesin

HO‐1havebeenassociatedwithvariousAPOssuchasrecurrentmiscarriage,FGRand

preeclampsiaaswellasmorphologicalchangesintheplacentaandelevationsinmaternal

bloodpressure.ThebioactivemetabolitesofHO‐1,COandbilirubin,mayprotectagainst

preeclampsiathroughtheirvasodilatorypropertiesandthesuppressionoftheanti‐

angiogenicfactorsFltrespectively[171,172].

3.3.3Fetalgrowthrestriction

FGRhasmanycauses,howeveroftenarisesfromplacentalinsufficiencydueto

compromisedsupplyofoxygenandnutrientstothefetuswhichmayhavebothshortand

long‐termhealthconsequencesontheoffspring[51,82,195].FGRisstronglyassociatedwith

earlyonsetormoreseverecasesofpreeclampsia,andthereisaclearetiologicallink

betweenIUGRandDDPasitinvolvesabnormalplacentationandreduceduteroplacental

bloodflow(Figure2)[62,70].Alternatively,perturbedcalciumhomeostasiscaninduce

chroniclow‐levelstresswithintheendoplasmicreticulumleadingtosuppressedprotein

synthesisandareducedgrowthtrajectoryoftheplacenta[70].Cadmium,anenvironmental

toxinandhighlypresentincigarettesmoke,isamajorantagonistofcellularcalcium

activities(transport,uptake,binding),aswellasthetransferofothernutrientsandzinc

homeostasiswithintheplacenta[134,185,196].Furthermore,cadmiumisaknown

endocrinedisruptorshowntoimpairhormonesynthesisintheplacentaincluding

progesteroneandleptin[49,175,186].Bothsmokingandairpollutionexposurewere

associatedwithlowerbirthweightsalongwithlowbloodprogesteronelevelsandhigh

placentalcadmiumconcentrationscomparedtoanon‐exposedcontrolgroup[135].

3.3.4Spontaneouspretermlabourandbirth

Inflammationisproposedasonepotentialmechanismleadingtospontaneous

pretermlabour,bothwithintactmembranesorPROM.Theclassificationofpatientswho

deliverpretermcanbecategorizedintotwonon‐mutuallyexclusiveclusters;thosewho

presentwithinflammatorylesions(e.g.acutechorioamnionitisandfunisitis)andthosewith

15

vascularlesionswhotendtohavelongergestationalperiods[61].Theconsequenceof

uteroplacentalischemiaasaresultofsuchlesionswilldependontheseverity,thetiming

anddurationoftheinsult.Whileacompleteblockageofuterinearterieswillleadtofetal

death,lesssevereischemiawillresultindifferentclinicalphenotypesasaresultofadaptive

mechanismsforfetalsurvival.Thismayincludefetalgrowthrestrictionifchronic

underperfusionofoxygenandnutrientspersists,theonsetofmaternalhypertensionto

sustainorincreaseuterinebloodflow,and/ortheinitiationofpretermlabourasa

maternal/fetaladaptationtocontinuedgrowthrestrictioninutero(Figure2)[61,197].

Cardiovascularlesionsindicatingthrombosisandatherosisareshowntobeindirectly

causedbyexposuretoPM2.5andUFPsviainflammatoryand/oroxidativeinjury[97].

4.0ThePhysicalandSocialEnvironmentandtheirRelationtoAdversePerinatalOutcomes

4.1.PM‐inducedoxidativestressandinflammatorymechanismsExposuretoPM2.5anditsconstituents,includingPAHsandmetals,induceoxidative

stressandinflammationinmanybiologicalsystemsthroughvariousmeans(Figure3)

[48,77–79,97,176,177,198].OnemethodisthedirectgenerationofROSfromfreeradicals

andoxidantsonparticlesurfacesincludingsolubletransitionmetalssuchasiron,copper,

chromiumandvanadium.Asmentionedabove,freeironcanreactwithavailablesuperoxide

orhydrogenperoxidetoformhighlyreactivehydroxylradicals[70,77].PAHsandother

organicmoleculesabsorbedtoPM2.5andUFPsmayaccountforalargeproportionoftheir

oxidativepotentialduetotheirabilitytoenterthecellanddisruptthemitochondria[79].

AlteredfunctionofmitochondriamayproduceexcessquantitiesofNADPH‐oxidasewhichin

turngenerateslargeamountsofcellularsuperoxide,aprocessalreadyinoverdrive

throughoutpregnancybutparticularlyinthefirsttrimester[70,77].Interpolatedambient

PM10exposurewasshowntobenegativelyassociatedwiththenumberofplacental

mitochondrialDNA,amolecularmarkerofmitochondrialdisruptionandinflammation.This

associationwasreversedwithincreasingdistancefrommajorroads,aproxyfortraffic‐

relatedairpollution[48].

16

Alternatively,PM/PAHmediatedoxidativestresscanbeinducedbytheactivationof

theinflammationsystem.Immunotoxiccompoundscanpromotethereleaseofpro‐

inflammatorycytokines,TNF‐αandCOX‐2,whichinturnactinapositivefeedbackloopto

generatemoreROSandoxidativestress[77].Forexample,modelledPM10andPM2.5

exposurehasbeenpositivelyassociatedwithelevatedC‐reactiveprotein(CRP)levels,a

biomarkerofsystemicinflammation,inbothmaternalfirsttrimesterbloodandfetalcord

bloodinadose‐dependentmanner[176,200].CRPisproducedintheliverandpartofthe

acute‐phaseresponsereleasedduringinflammatoryreactionsfromcytokinesproducedin

thelungs.RaisedCRPisariskfactorforcardiovasculardiseaseasamarkerofunstable

atheromatousplaguesleadingtothrombosisandischemicevents[97].Exposuretodiesel

exhaustinhealthyhumanvolunteersproduceddefinedhealtheffectsinadditionto

pulmonaryinflammation,includingsystemicinflammation,pro‐thromboticchangesand

othercardiovasculareffectsconsequentofpro‐inflammatoryevents[99,201].Thishyper

pro‐inflammatorystate,alongwithoxidativestress,ishypothesizedtocontributetoseveral

APOs[69,70,174,181,202].

Figure3:ProposedpathwaysofparticulateairpollutioncontributingtooxidativestressandinflammationleadingtoadversepregnancyoutcomesExposuretoPManditsassociatedconstituentsoftransitionmetals,PAHsandotherorganicmoleculesaffectthecardiovascularandmetabolicsystemswhicharehighlyactivethroughoutpregnancy.Forexample,detoxificationofPAHsandotherorganictoxinsactivateAhRsignallingresultinginadditionaloxidativestressifantioxidantdefensesarelimitedorimpaired[55,79,98,108,109,199].

17

Indirectly,thecellulardetoxificationofPAHscaninduceoxidativestressand

cytotoxicitybyformingpotentROSmetaboliteby‐products.Specifically,PAHsandother

organicxenobiotics(notablyPCBsanddioxins)aredetoxifiedbythecytochromeP‐450

(CYP)superfamilyofPhaseIandPhaseIImetabolizingenzymes.Theexpressionofthese

enzymesarehighlymodulatedbygeneticpolymorphisms,steroid/sexhormonessuchas

glucocorticoids,insulin,estrogensandprogesterone,andmicronutrient/dietary

deficiencies[74,75,128,203,204].Furthermore,hypoxia,infectionandinflammationare

shown,ingeneral,todown‐regulateCYPenzymeswhichmayaffecttheclearanceand

bioavailabilityofgrowthfactors,hormones,drugsandtoxins[203,205].CYPhasnumerous

isoformswhichareexpressedinmanytissuesespeciallytheliver.CYP1A1istheonly

isoformalsosignificantlyexpressedintheplacentathroughoutpregnancyresponsiblefor

metabolizingsteroid/sexhormones,growthfactorsandfattyacidsinadditiontotoxins

[75].Theseexogenousandendogenoussubstancesactasligandstoactivatethearyl

hydrocarbonreceptor(AhR),atranscriptionfactorthatmediatesthebiotransformationof

suchligands(PAHs,estradiol,etc.)intomorepolarandbioavailablemetabolitesbyup‐

regulatingCYPenzymes(seeendnote4).However,certainmetabolitesofPAHs(e.g.o‐

quinones,areneoxideanddiolepoxide)bindtoDNA,RNAandproteinmacromoleculesto

formtoxicadductsthatdisruptDNAreplicationandareconsideredmutagenic[72,75].Such

DNAadductshavebeenfoundinnewborncord‐bloodpositivelycorrelatedwithmaternal

exposuretoPAHs[50].PAHshavealsoshowntosignificantlydecreasetheaccumulationof

trophoblaststemcellsintheearlyplacentatherebylimitingtheirdifferentiationintoother

celltypesvitalforhormonesynthesisandongoingplacentaldevelopment,aprocessthat

couldcontributetoDDP[72].DirectprenatalexposuretoairbornePAHshasbeen

associatedwithFGRwithanincreasedexposure‐relatedriskinthefirsttrimester

[206,207].Secondary(PhaseII)metabolizingenzymesarerequiredtofurtherdetoxify

reactivePAH‐metabolites,towhichtheirinefficientclearanceresultsinprolongedexposure

leadingtosustainedcytotoxicityandmutagenicity.PhaseIIenzymesincludeglutathiones‐

transferases(GSTs),UDP‐glucuronosyltransferases(UGTs),NAD(P)H‐dependentquinone

oxydoreductase‐1(NQO1),aldehydedehydrogenase‐3(ALDH3)[75,205].

4.2MaternalDietandMicronutrientIntake

Adequatedietandmicronutrientstatusprovidesresilienceagainstoxidativestress

andinflammationcausedbyvariousexposuresincludingairpollution,allostaticstress,

infectionorsmoking(Figure4)[55,118,128,129,131,143].Manymicronutrientssuchas

18

essentialtracemetalsarevitalco‐factorsinseveralantioxidantenzymesystems.For

example,copperandzincarenecessaryintheproductionofSOD.Similarly,seleniumandits

incorporationintotheaminoacidselenocysteineisrequiredforthefunctionalityofall

selenoenzymes,includingGPxandGST.Thus,seleniumisessentialinseveralaspectsof

humanhealth,particularlyconditionsinvolvingoxidativestressandinflammationsuchas

CVD,immunefunction,cancerandreproduction,butalsothyroidregulationandbrain

diseases[208,209].

ROSmayhavedirecteffectsonoocytequalityandappearstobemodulatedbydietary

antioxidantsupplements[118].Womenwhoareobesetendtohavehigherratesof

infertilitythatcorrelatewithincreasedlevelsofoxidativestressbiomarkersintheirblood

asexcessglucoseavailabilityleadstohighermitochondrialROSsynthesis[70,118].

SeleniumdeficiencyandcorrespondingreducedGPxactivityhasbeendocumentedincases

ofrecurrentmiscarriageandspontaneousabortions[210–212],andhasalsobeen

associatedwithpreeclampsiaandpretermbirth[213,214].However,giventhesupposed

roleofoxidativestressinpreeclampsia,treatmentwithcertainantioxidants(notably

vitaminCandE)hasnotproducedreliablepreventativeresultsinexperimentaltrials[69].

Onehypothesisisthatinappropriateantioxidantregimentand/oradministrationtoolatein

gestationareresponsibleandnewtherapeuticcandidatesincludemelatoninandselenium

[118].Interestingly,nationalprogramsinFinlandandNewZealandfortifyingfoodwith

seleniumhasbeenassociatedwithsignificantreductionintherateofpreeclampsia[215].

Oxidativestressnegativelyaffectstheplacentaltransportofaminoacidsandglucose

[45].Furthermore,fattyacidsandlowdensitylipid(LDL)cholesterolsnecessaryforthe

placentalsynthesisofoestrogensandprogesteroneareparticularlyvulnerabletooxidative

injury[216].Regulationofplacentalnutrienttransportiscontrolledbyseveraldifferent

mechanism,includingimprintedgenes,placentalsignalingpathways,variouscytokinesand

hormonessuchasinsulin,leptin,glucocorticoidsandoestrogens(forreviewsee[45]).The

majorplacentaltransfermechanismsinclude:simplediffusionoflipophilicsubstances(e.g.

oxygen,CO2,fattyacids,steroids,fatsolublevitamins,anestheticgases),restricteddiffusion

ofhydrophilicsubstances,facilitateddiffusionviaamembraneboundcarrier(e.g.glucose

andothercarbohydrates),andactivetransportwhichrequiresenergy(e.g.aminoacids,

iron,calcium,andotherdivalentcations)[45,217].Placentalphysiology,includingspiral

arteryremodelingandplacentalvilloussurfaceareaaremajordeterminantsdictating

19

placentaltransportcapacity,andthedegreeofplacentaldevelopmentaldisruption

correlateswiththeseverityofobstetricalcomplicationsassociatedwithDDP[51,62].

Nutritionanddietcaninfluenceperinatalhealthinopposingdirections(i.e.itcanbe

anantagonistoragonist).Poor/under‐nutritionsuchashighfat/caloriedensefoodandlow

micronutrientintakeismoreprevalentamongwomenfromlowSESbackgroundswhich

maypartlyexplainhigherratesofsomeAPOs[12,139–142].Ontheotherhand,good

nutritionandsupplementalvitaminintakeiscapableofreducingthetoxicityofeveryday

environmentalstressorsaswellaspreventingcertainAPOsandcongenitalanomaliesas

shownwiththesuccessfulreductionofneuraltubedefectswithfolicacid[128,143,218].

Nutritionaland/orgenetically‐induceddeficienciesinfolateandvitaminsB6andB12can

disruptthehomocysteine‐to‐methioninepathwayresultinginhyperhomocyteinemia

(HHC),aknownriskfactorofcardiovascularmorbidities(thrombosis,lesionsandinfarcts)

andmarkersofoxidativestress[54,119,219,220].HHCmaysimilarlyaffectthehighly

vascularizedplacenta,andhasbeenassociatedwithdecidualvasculopathyandpreterm

birth[54,120].Omega‐3fattyacidsabundantfromeatingsalmonwasshowntoimprove

markersofoxidativestress[221],whichmayimpartneurodevelopmentalresilienceagainst

stressors[222,223].Dietaryphytophenolsfromfruits,vegetables,herbsandspiceshave

showntohaveantioxidantandanti‐inflammatorypropertiescapableofreducinginfection‐

inducedinflammatoryandcontractilepathwaysinhumangestationaltissues[129].

SignificantdifferencesinpregnancyoutcomesbetweenDominicansandAfricanAmericans

bothexposedtosimilarlevelsofPAHsinNewYorkcityneighbourhoodswerethoughttobe

duetohealthfuldietary/culturalpracticesintheDominicanimmigrantpopulation[206].

20

4.3MaternalSmokingandEnvironmentalTobaccoSmoke(ETS)ExposureMaternalsmokingduringpregnancyandexposuretoETSremaintobetwo

modifiableriskfactorswiththegreatestpotentialforbeneficialinterventions(Figure4).

TheirassociationwithnumerousAPOsincludingcongenitalanomaliesiswelldocumented

[127,144–146],ashavetheirassociatedprevalencewithindicatorsoflowSESandother

socially‐patternedriskfactors[147–149].ThemechanismsinvolvedleadingtoAPOshave

beenwellreviewed[132,134];however,it’snotablethatthetwomaintoxinspresentin

tobaccosmokearealsoconstituentsofPM(PAHsmoresothancadmium).Cadmium(Cd)

exposurereadilyinterfereswiththeactivetransportofessentialmineralstothefetus,

particularlyzincandcalcium[46,135,196,226–228].Cadmiumandlead(Pb)exposurehas

alsobeenshowntoreduceglycogenconcentrationstherebypotentiallylimitingavailable

glucosetothefetus[229].Cadmiumhasshowntodisruptplacentalleptinsynthesis,a

hormonewithseveralvitalfunctionsincludingplacentalangiogenesis,immunomodulation,

aminoacidandfattyacidtransportaswellasfetalpancreaticdevelopmentimportantinthe

regulationofinsulin‐likegrowthfactorsandfetalbodyfataccumulation[49,51].Finally,

synergisticeffectsinthegenerationofoxidativehydroxylradicalshavebeenobserved

betweentobaccosmokeandbothambientPM2.5anddieselexhaustparticlesspecifically

Figure4:ProposedpathwaysofhowthesocialenvironmentinteractstoproduceexcesssystemicandplacentaloxidativestressandinflammationleadingtoadversepregnancyoutcomesThepregnantwomanisnestedwithinandinfluencedbyneighbourhood/community‐levelfactorswhichcanexasperateorbuffertheindividual‐levelbiologicalandbehaviouralfactors[24,26,54,128,224,225].

21

[35].Interestingly,thecounterintuitiveassociationbetweensmokingandlowerriskof

preeclampsiawasrecentlyshowntovaryaccordingtothetimingandintensityofsmoking

[230].It’spossiblethattheincreasedexposuretocarbonmonoxide(CO)fromsmokingin

lategestationactsasavasodilatoratthesametimeinhibitsthereleaseofsFlt‐1,ahallmark

anti‐angiogenicfactorimplicatedintheendothelialdysfunctionofpreeclampsia[115,230].

4.4AllostaticStressandGlucocorticoidExposureReviewedelsewhere[163],thebrainistheprimarytargetandmediatingorgan

throughwhichSES‐relatedstresspathwaysaretranslatedtootherbodysystemsviathe

hypothalamic‐pituitary‐adrenal(HPA)axis.TheHPA‐axisisactivelyinvolvedinseveral

biologicalsystems,includingthecardiovascular,metabolic,immunologicalandendocrinal

effectsinboththemotherandfetustopromoteallostaticadaptation[165,231].Here,the

neuroendocrinehormonesoftheHPAaxis,corticotrophinreleasinghormone(CRH),

adrenocorticotropichormone(ACTH),andglucocorticoids(GC)respectively,coordinatethe

biologicalresponseviafeedbackloops.ThehumanplacentaisalsocapableofreleasingCRH

andotherneuropeptideswhichinteractwiththeHPAaxistoregulatethematernalstress

responseaswellasothernormalpregnancyfunctions[47].Properlevelsofin‐utero

glucocorticoidsareessentialforsuccessfulembryoimplantation,fetalorganmaturation

andtheinitiationoflabourwithglucocorticoidlevelsgraduallyincreasingoverthecourse

ofgestation.Normally,levelsofmaternalcortisolrisesharplyinthethirdtrimestercausing

thereleaseofplacentalCRHinapositiveadrenal‐placentalfeedbackloop.PlacentalCRH

stimulatesfetalcortisolsecretionwhichinturnsuppressesplacentalprogesteroneand

activatesthereleaseofprostaglandinsandoxytocintopromoteuterinecontractions

[47,232].However,earlyandincreasedlevelsoffetalglucocorticoidscanimpairgrowthand

predisposetoadult‐onsetdiseases[136,233,234].Theplacentalenzyme11β‐

hydroxysteroiddehydrogenasetype2(11β‐HSD2)protectsthefetusfromexcess

endogenousglucocorticoidsbyconvertingactivecortisolintoinactivecortisone.11β‐HSD2

ishormonallyregulatedmakingitsusceptibletoendocrinedisruptionfromchemicaland

non‐chemicalstressorssuchasmaternalanxiety,inflammation,infection,cadmium

exposureandlowcaloricintake[136,138,224,235,236].Placentalhypoxiaassociatedwith

PIH/PEhasbeenshowntosuppress11β‐HSD2activitywhichmaybeanadaptiveresponse

tocounteractcompromisedfetalgrowthbyallowingmorecortisoltoreachthefetusfor

organdevelopment.Lowconcentrations/activityof11β‐HSD2andhighlevelsofcortisol

22

havebeenassociatedwithPTBandFGR[136,237,238],twooutcomesalsoassociatedwith

maternalpsychosocial/mentalhealth[233,234,239].

Factorsaffecting11β‐HSD2activitythatareassociatedwithlowSESincludeallostatic

overloadleadingtotheexcessproductionofglucocorticoidsthatcanoverwhelmthefetal

protectivemechanism(Figure4)[136,231,240].Indirectly,allostaticloadiscapableof

disruptingthemetabolicsystem,leadingtoimpairedglucosetolerance,insulinresistance,

diabetesand/orobesity,allofwhichareriskfactorsforvariousAPOs[138,165,231].

Generalmaternalundernutritionand/oralowdietaryproteinintakehasbeenshownto

impairplacentalglucosetransportandinhibit11β‐HSD2activityinpregnantratsleadingto

FGR,indicatingapossiblemechanismthroughpoordiet[224,241].Additionally,cadmium

hasalsoshowntoinhibit11β‐HSD2activityinbothhumanandrodentplacentas[225],and

prenatalcadmiumexposurehasbeenshowntoincreasefetalcorticosteroneconcentrations

inratswhichresultedinreducedbirthweights[236].Thissuggestsapossiblemechanism

fromactiveorpassivetobaccosmokeexposureorambientPMexposure[135,242,243].

Collectively,it’spossibleforthecumulativeexposuresofPM,smoking,ETS,poordietary

intakeandotherSES‐relatedfactorstointeractthroughthesame11β‐HSD2mechanismto

increasetheriskofimpairedfetalgrowth(Figure4).

5.0DiscussionTheubiquitousexposuretoparticulateairpollutionanditsconstituents(e.g.PAHs

andmetals)isbutoneclassofenvironmentalcontaminantsthatcanactthroughoxidative

stress,inflammationand/orendocrinedisruptiontopromotedevelopmentaltoxicityand

adverseperinatalhealth[177,244,245].SummarizedinFigure2,aperturbedearlyin‐utero

environmentcanleadtodefectivedeepplacentationresultinginacascadeoffetal‐placental

adaptivemechanismscontributingtoarangeofpregnancycomplicationsandadverse

outcomes[60].Heretheunderlyingbiological,socialandphysicalriskfactorslikely

intersecttoproduceexcessiveoratypicaloxidativestress,inflammatoryresponseand

biologicalantagonismineitherinitiatingdefectivedeepplacentationpathologyand/or

contributingtotheseverityofitsphenotype.Socio‐economicdisparitiesareknownto

confoundtheenvironmentalexposureeffects;however,theymayalsoactaspotentialeffect

modifiersgiventheiroverlappingetiologicalmechanismswithPM2.5exposure.Whilethe

traditionalbiomedicalparadigmthatviewspopulationsasacollectionofindependent

individualshasyieldedusefulinformationregardingriskfactors,elucidatingthe

intersectingpathwaysinvolvedinAPOswillrequireplacingindividualbiologicand

23

behaviouraldeterminantswithinthesocialandspatialcontext[22,246].Itisnowwell

recognizedthatSESoperatesatmultiplelevelsoforganization,andneighbourhoodor

community‐levelfactorscanworktoeitherameliorateorexacerbatecertainriskfactors

[15,24–26].Thehealthymigrantparadoxexemplifiestheseeffectsinwhichhomecountry,

educationandneighbourhoodqualitiescombinetomodifytheexpectedperinataloutcomes

oftenobservedwithlowincomehouseholds[161,247].

TheSESriskfactorsthatoverlaporinteractwiththePM‐mediatedmechanisms

includesmoking,nutrition,andpsychosocialstressactingthroughtheHPA‐axisand

allostaticload.Giventhisknowledge,interventionsaimedatamelioratingthesefactorsmay

bethebestwaytocounteractthenegativeinfluencesoflowSESandairpollutionexposure

onfetaldevelopment.Maternalsmokingcontinuestobeoneofthemostmodifiablerisk

factorstolowertheriskofAPOs[134,147].Furthermore,maternalsmokingalsotendsto

interactnegativelywithnutrientintakeandstatus[133,248].Smokersingeneralhave

poorernutritionalprofilesthannon‐smokerswithbothbehaviouralandbiologicalfactors

independentlyaccountingforthedifferencesinmicronutrientssuchasfolateandessential

vitaminsandminerals[133,248–250].Whilesmokerstendtohavelowerdietarynutrient

intakes,theyalsohaveanacceleratedrequirementformicronutrientsduetoincreased

inflammatorycellturnovercausedbytheoxidativestressofsmoking,aneffectmore

pronouncedamongheavyandlong‐timesmokers[248].Theseinteractingeffectsof

smokingandnutritionarefurthercompoundedbytheirassociationwithotherindicatorsof

lowSESsuchasloweducationandincomecontributingtoallostaticload[139,251].

Nutrientintakemaybeameliorativeafteraninsulthasoccurredasshowninratmodelsof

fetalalcoholsyndromewhereanomega‐3fattyacidenricheddietreversedthecellular

effectsofprenatalethanolexposureonthefetalbrain[222].Thereforewithrespectto

policyinterventions,nutritionintheformofimprovedfoodsecurityandmicronutrient

intakemayservetocounteractthenegativeinfluencesofbothlowSESandairpollution

exposure[252–257].

Thecomplexmixtureofparticulateairpollution,especiallyPM2.5andUFPswhich

includesabsorbedPAHsandvariousmetals,alsoemergesasanimportanttargetforrisk

reductionandmanagement.Thedeservedscrutinystemsfromtheubiquityinthe

environment,themyriadofemissionsourcesandtheirestablishedassociationwithAPOs

[88,98,258].TheubiquityofPM2.5andUFPsintheenvironmentmeansthatahigh

proportionofpeopleareexposedresultinginahighetiologicalfraction.Therefore,evena

24

modestreductioninexposurewillhavealargepopulationeffectwithreductionofthe

societalcostsofAPOs[259].Notably,theirsourcesareprimarilylocal,suchasvehicle

emissionsandindustrialland‐use.Thismakesthemmodifiableriskfactorsthatcanbe

addressedatthemunicipalandprovincial/statelevelwithbetterurbanplanningtoreduce

vehicletraffic,increasingaccesstogreen‐spaceandenforcingairqualityregulations[260–

263].

Notunliketheaccumulationofevidenceonsmokingandhealthoutcomesorthatof

airpollutiononcardiovascularandpulmonaryhealth[264],theepidemiologicaland

toxicologicalresearchoverthepasttwodecadeshasestablishedaconsistentdose‐response

associationwithhighbiologicalspecificity,temporalityandplausibility[3,55,177].Takenin

concert,thesecharacteristicsandfurthercorroboratingresearchshouldlendstrengthfor

evidence‐basedpolicyforinterventionstrategiestargetinghighriskareasinorderto

reducetheenvironmentalburdenofdiseaseattributedtoparticulateairpollution

[98,265,266].

6.0ConclusionAdversepregnancyoutcomessuchasfetalgrowthrestrictionandpretermbirtharea

publichealthpriorityofglobalimportance.Wehavebroughttogetherthemultidisciplinary

literatureonthecurrentstateofevidencelinkingthephysicalandsocialenvironmentto

specificadversepregnancyoutcomes.Theevidencesuggeststhatvariousexposures,

whethersociallyorenvironmentallydetermined,maybeinterpretedbythefetoplacental

unitinsimilarwaysresultinginacommonpathologicalset‐upforadverseoutcomes,

namelydeficientdeepplacentation.Giventhisbackground,wellplannedfuture

epidemiologystudiesusingmultilevelmodelsexploringvariousbiologicaleffectsofthe

socialandphysicalenvironmentwillhavethepotentialtoprovidetheevidencetoestablish

crucialwindowsoffetalvulnerabilitywithanaimtoidentifyandmitigatemodifiablerisk

factors.

7.0Endnotes1‐ Implantationoftheblastocyst(0.1mminsize)beginsondaysixfollowingconception

inwhichitcompletelyembedsitselfwithinthematernaluterineendometrium,usually

ontheupperposterior(fundal)wall.Theperi‐implantationperiod(approx.day1‐20

post‐fertilization)isarguablymostcriticalperiodofreproductionastwo‐thirdsof

conceptionsfailatthistime,oftenunbeknownsttotheindividual[72].Individual

variabilityingeneticpredispositioninteractswithbothenvironmental(exogenous)

25

andmaternal(endogenous)stressexposuresthatcanleadtodefectiveplacentation

withsubsequentobstetricalcomplicationsand/orthedevelopmentofbirthdefectsvia

teratogenesis[71,72,245].Indeed,fiftypercentofconfirmedmiscarriages(pregnancy

losspriorto20weeksgestation)areduetoabnormalembryonickaryotype

(chromosomes),anaturalbiosensormechanismtorecognizeandeliminatedefective

embryos[267].Furthermore,ectopicpregnancies(implantationanywhereotherthan

uterinecavity)andplacentaprevia(implantationonthelowerinferiorsegmentofthe

uterusnearthecervix)areriskfactorsofmaternalmortalityormorbidityandareoften

eitherterminatedordeliveredpretermviacaesareanbirthrespectively[268].

Successfulimplantationandsubsequentplacentationinvolvesthecoordinated

differentiationofthetrophoblast(outercelllayeroftheblastocyst)intothreedistinct

cellpopulations,syncytiotrophoblasts(STBs),cytotrophoblasts(CTBs),and

extravilliouscytotrophoblasts(ECTBs)[68,217].TheSTBisthemainendocrine

hormonesynthesiscomponentoftheplacentaandrapidlyexpandsintothematernal

endometrium.TheCTBsarethemitoticallyactiveprogenitor(stem)cellswhich

migratein‐behindtheexpandingmassoftheSTBwhicheventuallygivesrisetothe

basalplateofthedefinitivediscoidplacentaaroundmid‐gestation.Finally,theECTB

aresubsequentlydifferentiatedfromtheadvancingCTBcellsbeyondthebasalplateto

infiltratetheunderlyingmaternaldecidualcellsanduterinespiralarteries.Attheend

ofthethirdweekofgestationthedefinitiveelementsofthediscoidplacentaarein

place:thechorionicplatefromwhichthevilliariseandfetalbloodwillflow,abasal

plateinwhichanchoringvilliareattached,andlacunar(intervillous)spacesintowhich

maternalbloodwillcirculate(Figure5)[217].Thehistologicalstructureofthehuman

placentaiscommonlydescribedashemochorialinwhichthefetal/embryonictissues

areindirectcontactwithmaternalblood.

Thefreeflowofoxygenatedmaternalbloodintotheplacentaalongwithantioxidant

enzymes(SOD‐superoxidedismutase,catalaseandGPx‐glutathioneperoxidise)

servesaphysiologicalrolefacilitatingmaternal‐fetalnutrienttransferandgas

exchange;howevertheconsequentialburstofROSmaynegativelyimpacton‐going

placentaldifferentiationandgrowth,suchasshallowtrophoblastinvasionofmaternal

spiralarteries,ifantioxidantdefencesaredepleted(Figure6)[68,192].

26

Figure5:Schemeofplacentalcirculationandfeatures(Grey’sAnatomylithographs)

(A)NormalPlacenta:Theextravillouscytotrophoblast(ECTB)cells(green)migrateintothedeciduaofthematernalendometriumwheretheyenterandremodelthematernalspiralarteriesfromanendothelialcelltypetoendovascularECTB(eECTB)cellslosingtheirresponsivenesstoendogenousvasoactivecompoundsandmaximizingbloodflowintoplacentalintravilliousspaces.(B)PreeclampticPlacenta:TheECTBinvasionintothedeciduaisshallowandlimited,withmanyECTBcellsinthebasalplateremainingattachedtoanchoringvilli(AV).Endovascularinvasionandremodellingofthespiralarteriesisabsentorreduced,thusconstrictingbloodflowandreactivetomaternalstimulussuchasstressandothervasoconstrictingagents.FV:floatingvilli.[269].ImagecourtesyofTheCuratorsoftheUniversityofMissouri(2011).

2‐ Deficientdeepplacentationisthoughttoresultinreducedandintermittentplacental

perfusiontherebyrestrictingbloodflowandlimitingoxygenandnutrientsupplytothe

fetus.Thematernaladaptiveresponseistoincreasebloodpressuretocompensatefor

therestrictedflow.Theensuingplacentalhypoxiainthelattertwotrimestersleadsto

theupregulatedexpressionandsecretionofsolubleantiangiogenicfactors(sFlt1and

sEng)whichantagonizeproangiogenicfactors(VEGF,PlGF,andTGF‐β1)causing

systemicendothelialdysfunctioncharacteristicofPIH/PE[115].

Figure6:Invasiondefectsinpreeclampsia

27

3‐ Theplacentaisamulti‐functionalorganandisresponsibleforthreemajorrolesatthe

maternal‐fetalinterface.First,theplacentaisanimmunologicbarrierbetweenmother

andfetus,andisthoughtthattheplacentaltrophoblastandmaternalimmunesystem

positivelyinteractforthesuccessofthepregnancy[44,173,217].The

syncytiotrophoblastactsthroughtheproductionofvariouscytokinesandchemokines

responsibleforthecoordination,migration,differentiation,andfunctionofthe

maternalimmunecells[182,183].Thisisachievedthroughanon‐linearseriesof

positiveandnegativefeedbackpathwayswiththestimulationandsuppressionof

moleculeswithbothpro‐andanti‐immunosuppressantproperties(interleukins,

integrinsandglcyodelin–seeTable1).Interferencewiththeproductionorsecretionof

thesesubstancesmayresultintheimpairedmaternalimmuneresponsetothesemi‐

alliographfetal‐placentalunitleadingtoseveralAPOsincludingearlypregnancyloss,

PIH/PE,andPPROM[61,134,175,185,186].

Thesecondmajorfunctionoftheplacentaisitssynthesisofsteroidandprotein

hormonesvitalintheprogressionofthepregnancy(Table1).Mostofthecirculating

substancesinTable1appeartobesynthesizedbythesyncytiotrophoblast.

Interferencewiththeproductionorsecretionofthesesubstancesbyxenobiotics(e.g.

cadmium,lead,arsenic)mayresultinseveralAPOs,suchasearlypregnancyloss,

PIH/PE,impairedimmunoprotectionandPPROM[134,175,185,186,236].Forexample,

thePM‐mediatedoxidationoflow‐densitylipoproteins(LDL)mayinterruptacascade

ofeventsnecessaryforthefetalproductionandexportofoestriol,quantitativelythe

mostsignificantoestrogeninmaternalcirculationinlatepregnancy[97,217].Chronic

oracutematernalpsychosocialstressalsohasthepotentialtodisturbimmuneand

endocrinesignallingpathwaysviathehypothalamic‐pituitaryadrenal(HPA)axis

[47,72,136,138].

Thethirdmajorfunctionoftheplacentainvolvesvarioustransfermechanisms

similartothosefoundinotherepithelialsystems.Themajortransfermechanisms

include:simplediffusionoflipophilicsubstances(e.g.oxygen,CO2,fattyacids,steroids,

fatsolublevitamins,anestheticgases),restricteddiffusionofhydrophilicsubstances,

facilitateddiffusionviaamembraneboundcarrier(e.g.glucoseandother

carbohydrates),activetransportwhichrequiresenergy(e.g.aminoacids,iron,calcium,

andotherdivalentcations),receptor‐mediatedendocytosisandexitmechanisms

[45,217].Regulationofplacentalnutrienttransportiscontrolledbyseveraldifferent

28

endogenousmechanism,includingimprintedgenes,placentalsignalingpathways,

variouscytokinesandhormonessuchasinsulin,leptin,glucocorticoidsandoestrogens

(forreviewsee[45]).Manyoftheabovelistedfactorscanbeindirectlyaffectedby

exogenousmediators(e.g.PMandPAHexposure,stress).However,exogenous

mechanismswithdirectmodesofactiononnutrienttransportincludecadmium(Cd),

lead(Pb)andoxidativestress.Forexample,Cdexposurefromtobaccosmoke,air

pollutionand/ordietreadilyinterfereswiththeactivetransportofessentialminerals

tothefetus,particularlyzincandcalcium[46,135,196,226,227].CdandPbexposure

hasbeenshowntoreduceglycogenconcentrationstherebypotentiallylimiting

availableglucosetothefetus[229],andoxidativestressnegativelyaffectstheplacental

transportofaminoacidsaswellasglucose[45].

29

Table1:Biologicalfactorsinvolvedinpregnancy,theirroleandup/down‐regulation

Name FunctionandRole up/down‐regulation Ref.Progesterone,oestriol(E3),oestrone(E1),17β‐estradiol(E2)

- Steroidhormones,regulatemenstrualcycle,preparesendometriumforimplantation.

- BiosynthesizedbyplacentausingLDLcholesterolinmaternalblood

- Up‐regulatesmanyotheritemsbelow

↓byCd,otherheavymetals↓byPMviaoxidizedLDLcholesterol↑E2metabolismtoreactivemetabolitesviaPAH‐inducedCYP450

[75,216,229,270]

HumanChorionicGonadotrophin(hCG)

- GlycoproteinHormone- Releasedbyblastocyst,initialpregnancyrecognitionandcorpusluteumsurvival

- Detectablebyweek3,peaksweek11- Stimulatesinitialprogesteronerelease- Immunetolerance,trophoblastdifferentiationandangiogenesis

↓byCdexposure,indirectlyviareductionofleptin↓inchorioamnionitis↑uNKcellproliferation↑byGM‐CSF↑byIL‐1β,TNF‐αviaIL‐6↑LIF,VEGF,MMP‐9

[49,182,186,216,271]

Granulocytemacrophage‐colonystimulatingfactor(GM‐CSF)

- Cytokine- PromotesDNAproliferation,differentiationandplacentalsecretoryactivity

- Requiredforoptimalplacentaldevelopment,fetalgrowthandsurvival

- Synthesisinuterineepithelialcellsisregulatedbyestrogen

- Lowbloodconcentrationlevelsinwomenwithrecurringmiscarriages

[271,272]

PlacentalandVascularEndothelialGrowthFactor(PlGF,VEGF)

- Pro‐angiogenicgrowthfactorsforvasculardevelopmentofembryo,placentaandspiralarteryremodelling

- Promotesvasodilation- LowPlGF:sFltratioinpreeclampsia

↑byhCG,hypoxiainearlypregnancy↓bysFltandsEngviaexcessoxidativestress/inflammation

[63,194]

Interleukin(IL),TumourNecrosisFactor(TNF),&LeukemiaInhibitoryFactor(LIF)

- Largefamilyofpro/anti‐inflammatorycytokines(non‐enzymiccellmessenger)

- Involvedinallaspectsofpregnancy- Pro‐inflammatory:IL‐1α/β,IL‐6,TNF‐α- Anti‐inflammatory:IL‐10,IL‐4- LIFessentialforimplantation,releasedbyuNK,regulateinvadingpTBCs

↓IL‐6byhCG,progesterone↑IL‐6byexercise↑LIFbyhCG&IL‐1β↑↓byheavymetals

[174,175,182,183,272]

Prostaglandins(PG‐E2)

- Increasevascularpermeabilityinthedeciduaandatimplantationsite.

- Stimulatescontractions,labouronset- RegulatedbyCOX‐2

↑byIL‐6,IL‐1β,TNF‐α↑byinfections↓bydietaryphytophenols

[129,273]

Glycodelin‐A,Galectins

- EarlysecretionbyuNKcellsmodulatesendometrialimmuneadaptation

↑byhCG,progesterone↓bystress

157,158,162]

Integrins - Cellsurfacereceptorandadhesionmolecules,importantinEVTinvasionofdeciduaandECMremodelling

↑byIL‐6andVEGFAlteredbyCd

[63,185,193]

GlucocorticoidSteroids(GS)e.g.cortisol&corticosterone

- UpregulatedwithHPA‐axisactivation- Involvedinimplantation,fetal‐placentalgrowth,organdevelopment,utero‐placentaladherence,onsetofparturition

- Excessisdetrimental,fetalexposureregulatedbyplacental11β‐HSD2

Hormonallyregulated↑PG‐E2,hCG↑withbloodPb,lifestress↑byIL‐1β,IL‐6↓uNKcells↓11β‐HSD2byCd,hypoxia

[29,136,182,236]

Acronymsused:11β‐HSD2:11β‐hydroxysteroiddehydrogenasetype2;Cd:Cadmium;COX‐2:cyclo‐oxygenase‐2;CYP450:cytochromeP450;ECM:extracellularmatrix;EVT:extravilloustrophoblasts;HPA:hypothalamus‐pituitary‐adrenal;LDL:low‐densitylipoprotein;MMP:matrixmetalloproteinase;Pb:Lead;sEng:solubleendoglin;sFlt:solublefms‐liketyrosinekinase;pTBC:placentaltrophoblastcells;uNK:uterinenaturalkiller.

30

4‐ CrosstalkfromAhRtoothersignallingpathwaysinthefetal‐placentalunitincludes

PAH‐inducedAhRinteractingwiththehypoxia‐induciblefactor(HIF)which,depending

onthetimingofexposure,mayalteroxygentensioninearlypregnancyandresultin

miscarriageorsubsequentlyreducedtrophoblastinvasionleadingtoPIH/PEand/or

IUGR[75].Moreover,dependingonthesourcemixture,PAHsarecapableofinhibiting

theirownmetabolismintoharmfulmetaboliteswhichmayexplainthediscrepanciesin

laboratorytestingofsinglePAHchemicalsversuswholemixtures[75].Similarly,the

differentialAhRinductionbetweenCYP1A1andCYP1B1dictatesmetabolitetoxicity.

Forexample,estradiolmetabolismbyCYP1B1converts17β‐estradiol(E2)to

carcinogenic4‐hydroxyestradiol(4‐OHE2)whichformsDNA‐adductsandgenerates

ROS.Incontrast,CYP1A1convertsE2intonon‐carcinogenic2‐hydroxyestradiol(2‐OH

E2)whichisshowntobemarkedlystimulatedinplacentasofsmokersviatheup‐

regulationofCYP1A1.

IncreasedCYP1A1levelsaresignificantlyincreasedinex‐smokerscomparedto

thosewhoneversmoked.PAHsarelipophilicandtendtobestoredinthekidneys,liver,

andtoalesserdegreethespleen,adrenalglandsandovaries.ThusPAHsmaybere‐

releasedintothebloodcirculationduringpregnancy,similartothemobilizationoflead

frommaternalbonestorespreandpostpartum,[75,274].ToxicPAH‐DNAadductsfrom

smokingaredetectableinovariangranulosa‐luteincells,oocytesandspermatozoa.

PaternaltransmissionofalteredDNAtopreimplantationembryoswereshownin

assistedcontraceptionexperimentsandmaycompromiseembryonicdevelopment

suchasfailedimplantationorearlypregnancyloss[275].Thereforeperhapsthe

increasedratesofinfertilityandotherrisksassociatedwitholdermaternalagehasless

todowithbiologicaldeclineandmoretodowithincreasedtoxicbodyburdensinboth

menandwomen.Exposurestypicalofurbanairpollutionandmoderatepre‐conception

maternalsmokinghaveshowntoadverselyaffectplacentalmorphologyinmicemodels

[276,277].

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Chapter2:MethodologicalBackground“themajorcausesofdiseaseandsufferinginthepopulationarefirmlyrootedinthebehavioural,social,andpsychologicalworldsinwhichpeoplelive…[and]areembeddedinmultipleenvironmental,geographical,institutional,andeconomiccontexts.”‐Kaplan,G.A.ResearchLectureattheNobelForum,April8,1999,p.2[1].

1.0IntroductionWhiletherootsofasocialapproachtohealthcouldarguablybetracedbacktothe

sanitarycampaignsofthe19thcenturyinBritain,thefocuswasprimarilyonimproving

environmentalhygienetopreventtransmissionofpathogeneticdiseasestypicalofnon‐

industrializedstates.ItwastheWhitehallstudywithaLancetpaperin1978that

definitivelyestablishedtheepidemiologicalassociationbetweenchronicdiseasestatusand

socialclassinamodernindustrializedsetting[3].Byfollowingaprospectivecohortof

17,530civilservantsinLondonforsevenyears,itshowedthatriskfactorsforcoronary

heartdisease(CHD)includingsmoking,leisureexercise,plasmacholesterol,bloodpressure,

heightandweightonlypartiallyexplainedoccupationalclassdifferencesinCHDmortality

[3].

Aroundthesametime,JohnCasselproposedina1976lectureseriespaperthat

psychosocialstresswithinthecontextofthelargersocialenvironmentiscapableofaltering

diseaseresistanceofthehosttotheubiquitousmicrobiologicandphysiochemicalagentsin

theenvironment[4].Casseladvancedtheconceptsofseveralscientists,notablyReneDubos

forhis1965bookManAdaptingandL.E.Hinkleforhis1973paperTheConceptofStressin

theBiologicalandSocialSciences,fromwhichhequotes“Itisevidentthatanydisease

process,andinfactanyprocesswithinthelivingorganism,mightbeinfluencedbythe

reactionoftheindividualtohissocialenvironmentortootherpeople”[4][p.3].Hegoeson

toarguethatthepsychosocialprocessorstressorisunlikelytohaveadirectpathologywith

etiologicspecificityoradose‐responserelationshipforanygivendisease;butratherthat

theseprocessescaninpartcontributetoalldiseaseindirectlyviaalteredneuroendocrine

imbalance[4].Fortyyearslater,researchisjustnowstartingtoelucidatethepossible

mechanisms[5,6].

WithrespecttotheWhitehallstudy,theclearmortalitygradientsbysubtle

differencesinsocialpositionwithinwhite‐collaroccupationswasgroundbreakingandin

partledtowhatisnowtermedasthesocialdeterminantsofhealth.Repeatedtwentyyears

laterwithanewcohort(theWhitehallIIstudy),theresultsweresubstantiatedwhich

showednodiminutioninsocialclassdifferencesinmorbidity[7].

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Thefollowingsectionwilldiscusstwoprincipleissuesinobservationalepidemiology

1)theassessmentsocio‐economicstatus(SES),whatitisandhowtomeasureit;and2)

considerationofhealthoutcomedata,specificallyperinatalhealthdataandthedata

containedintheBCPerinatalDatabaseRegistry(BCPDR).Thesubsequentsectionthen

shiftstoexposureassessment,specificallyscenario‐basedlanduseregressionmodels,and

theirusewithinthecontextofpopulation‐basedepidemiologicresearchsuchasthis

dissertation.

2.0Socio‐economicStatusBackgroundSocio‐economicstatus(SES),orsocio‐economicposition,isanumbrellaterm

generallyusedtomeasuresocialinequity.Thereareseveralvariablesoftenusedas

indicatorsofSES,themostcommonbeingincome,occupationand/oreducational

attainmentmeasuredatboththeindividualandpopulationlevel[8–11].Theindirect

mechanismsbywhichSESvariablesoperatetoaffectindividualandpopulationhealthare

notfullyunderstooddespitethelonghistoryofresearchsupportingtheassociation[12–

16].ItisimportanttorecognizethatthecausalpathwaysofSESoperateatmultiplelevelsof

hierarchy(individual,household,neighbourhood,city,etc.).Socalled‘upstream’

determinants(incomeinequality,impoverishedcommunities)leadto‘downstream’

determinantsormediatingexposures(unhealthybehaviours,allostaticload)thatexistin

relationtooneanothertoinfluencediseaseriskasopposedtobeingindependentlyacting

phenomena[17].Forexample,aperson’sfoodchoiceisinfluencedbytheavailabilityand

accessibilityofhealthyfood(orlackthereof)intheneighbourhoodorcommunity,the

consumptionofwhichinturnreinforcesitsavailabilityinstores,creatingapositive

feedbackloop[18,19].Further,aperson’sbehaviourisinfluencedbytheprevalenceofthat

behaviouramongtheirpeers[20]whichthennormalizesthatbehaviourwithinthesocial

environmentwithpotentialforintergenerationaltransmission[21].Thesetwoexamples

representtwodifferenttypesofdetermination,reciprocalcausationandstructural

causationrespectively[22],andmaysubvertattemptstodisentangletheindividual

(compositional)factorsfromthesocial(contextual)factorsunlesscarefulefforttomodel

thesehierarchicalphenomenaisundertaken[23–26].

2.1MeasuringSocio‐economicStatus

Whenplanningtheconceptualmodelitisimperativetoconsidernotonlythelevelof

measurement(compositional,contextual),butalsothemannerinwhichSESismeasured

53

(absoluteorrelative),itspresumedroleinthemodel(confounderorriskfactor),and

how/ifitwillbeadjustedfor[10].ThethreemainindicatorsofSES(income,education,and

occupation)areinterrelatedbutarenotnecessarilyinterchangeable,andusingone

measureoveranothermayaffecttheresultsandmisleadinterpretations[27–30].Guidance

onselectingameasureofSEScanbevettedusingninecriteriaprovidedbyLiberatosetal.

(1988).Theyinclude:conceptualrelevance(relativestatusorabsoluteclassranking),its

roleinrelationtootherriskfactorsandtheoutcome,itsapplicabilitytothestudy

population,itstimerelevanceandstability,itsreliabilityandvalidity,whetherornotthere

aremultipleversussingleindicators,discreteversuscontinuousmeasures,itssimplicity,

anditscomparabilitywithotherstudies[10].

InequitiesinAPOsacrossracial/ethnicminoritystatusoftenparallelSESdisparities.

Thishasbeenobservedinmanymultiracialcountries,withevidencetosuggestboth

confoundingandeffectmodification.Black‐WhitedifferencesinPTBriskpersistafter

adjustmentforSESdifferences[17].Similarly,inCanadaAboriginalstatuscorrelateswith

manyuniqueupstreamdeterminantsoflowSESgiventhecolonialhistorywhichresultsina

disproportionateincidenceofAPOsafteradjustmentofSESdifferences[31–37].However,

lowSESremainsassociatedwithAPOsevenwithinracial/ethnicpopulationsandwarrants

furtherinvestigationintopossibleeffectmodificationbyneighbourhood‐levelfactors[17,

30].Forexample,the‘healthymigrantparadox’inMontreal,Quebecwasfoundtodepend

onthecountryoforigin,theirlevelofeducationandvariousneighbourhoodfactors[38].

Alternatively,truebiologicalandgeneticdifferencesmaypartlyaccountforthe

observeddiscrepancies;andthepotentialformisclassificationofnewbornsaseither

normal,SGAorlarge‐for‐gestationalage(LGA)mayhaveimplicationsoninterpretingrisk

rates[39].Forexample,FirstNationsbirthsinBritishColumbia(BC)tendtohave

significantlyhighermedianandmeanbirthweightsatallgestationscomparedtoallbirths

inBCdespitehavinglowerSESvaluesingeneral[40].Conversely,infantsofChineseand

SouthAsiandescentborninBCandelsewherehavesignificantlylowerbirthweightsthan

infantsofEuropeandescent[39,41].Usingthestandardprovincialbirthweightchartfor

newbornclassification,lessthantenpercentofFirstNationsinfantswouldbeconsidered

SGA‐10resultinginfalse‐negativesandthepotentialneglectofhealthrisksfacedbysmall

babiesmisclassifiedasnormalgrowth.Theadoptionofethno‐specificorcustomizedfetal

growthchartsmaybewarrantedinordertopreventthepotentialmisclassificationof

newbornsasSGAorLGA[42];however,thebenefitsofcustomizedbirthchartscontinueto

54

bedebated[43–45].Theuseofasmallercut‐point,suchasthe3rdor5thpercentileforbirth

weightwouldreducetheriskoffalse‐positivebycapturingtrulygrowthcompromised

newborns.

Inperinatalepidemiology,theuseofmaternaleducationasanindicatorofSEShas

beenshowntobestrongerthan,andindependentof,neighbourhoodincomeinrelationto

APOs[46].Bothindividual‐levelandarea‐basedmeasuresofmaternaleducationarestrong

predictorsofAPOs,regardlessofwhethereducationwasevaluatedasarelativeorabsolute

inequalitymeasure[47].Maternaleducationisanupstreamdeterminantthatpredicts

manydownstreamexposuresassociatedwithAPOs,includingheavysmoking,druguse,low

levelofprenatalcare,stressfulwork,infectionsandpsychosocialfactors[17].Low

educationandneighbourhoodSEShavebeenassociatedwithnutritionallypoorfoods

choicesandsuboptimalfruitandvegetableintake[19].

2.2Contextual(population‐level)MeasuresofSES

Facilitatedbydevelopmentsinstatisticalsoftwareprogrammingandadvancesin

computingspeed,thepopularizationofmultilevelmodelsoverthepasttwodecadeshas

spawnedaresurgenceofinterestintheanalysisofpopulation‐levelmeasuresofSESon

health[11,48,49].Ofparticularinterestistheabilitytoestimatecoefficientsofthe(cross‐

level)interactionsbetweenlevel‐onepredictorsoftheindividualandlevel‐twopredictors

measuredatthegrouplevel[23,50,51].Thecommonterminologyusedsynonymouslywith

‘contextual’includesneighbourhood,group,level‐2,andarea‐based.Level‐2variablescan

representgeographichierarchies,suchasneighbourhoods,cities,watersheds,butcanalso

representtimepointssuchasinlongitudinalstudies,classrooms,schools,oranygrouping

orclusteringvariable.Hierarchicalmultilevelmodelsarereviewedinmoredetailinthe

followingsection.

Thereareseveralwaystocreateorobtaingrouplevelmeasures,nationalcensus

beingthemostcommondatasource.Similartoindividuallevelmeasures,themost

frequentlyusedareincomeandeducation,oftenrepresentedbythegroupmean.The

percentageofunemployedisacommonoccupationalmeasureused.Compositionalindices

basedonthestatisticalweightingofseveralindicatorvariableshavebeenregularlyusedas

theycanincorporatethemultipledimensionsofSESinaneighbourhood[10].Theconcept

ofmaterialdeprivationreferstotheabilitytoaccessmaterialgoodsandconveniences,

whilesocialdeprivationreflectsthedegreeoffamily‐communitycohesionandsocial

interactions.Withtheaidofgeographicalinformationsystems,area‐baseddeprivation

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indiceshavebecomeacommontooltoassesspatternsanddegreeofSESinequalitiesin

relationtohealth[52,53].Inthisdissertation,IuseanSESindexproducedbyChanetal.

specificallyforthestudyofhealthoutcomesrelatedtoenvironmentalpollution[54].This

indexreflectsmoredimensionsofSESthanthedeprivationindexproducedbyPampalonet

al.[22]byusing22SEScensusvariablesincludingculturalidentitiesandhousing

characteristics,andwasshowntoperformbetterincapturinggradientsinprevalenceof

pregnancyoutcomes.ItalsohadmorecompletespatialcoveragecomparedtothePampalon

index,greatlyreducingthenumberofimputedvaluesforruralcensusDAs.

Indicatorsusedtoconstructadeprivationindexshouldbeassociatedwithhealth,be

relatedtoSES,andbeascertainablebysomestandardizedgeographiccensusarea.Itis

possibletocreategroup‐levelvariablesusingindividual‐levelindicatorsofSESsuchas

education.Thiscouldbeaverageyearsofeducationforadefinedneighbourhoodorthe

percentofthepopulationwithoutahighschooldiploma.Issuesregardingpotential“same

sourcebias”aregenerallytheresultofpoordataqualityand/orhighnumbermissing

values.Arecentstudycomparingindividual‐levelderivedmaternalneighbourhood

educationandacensus‐basedmeasureofeducationfoundthatpotentialbiaswasminimal

yieldingsimilarassociationswithPTB[47].

Onearea‐basedmeasurethatconfoundsandinteractswithmeasuresofSESisrural

location.Thereisnoabsolutedefinitionofruralotherthanbeing‘noturban’,butthis

dichotomizationcanmaskunderlyingtrendsinperinatalhealth[55–59].Definitionsof

ruralareoftenbasedonpopulationsizeanddynamicsandtheinterrelationshipofresidents

betweenotherlocations,particularlylargercitiesandmetropolitanareas.Commuterflows

and“metropolitaninfluencedzones”havebeendevelopedtoclassifyruralinrelationtothe

nearesturbancenteronacontinuousscaleusinghierarchical‐relatedcensusgeographic

areas(i.e.therural‐urbancontinuum)[60,61].InthisdissertationIuseStatisticsCanada’s

MetropolitanInfluenceZone(MIZ)methoddescribedabovetodefineruralresidenceintoa

dichotomous(CensusMetropolitanAreas(CMA)andCensusAgglomeration(CA)areas

versusCensusSubdivisions(CSDs)thatlieoutsidetheseareas)aswellasafivevalue

categoricalvariablerangingfromCMA/CAtothefourzonesofinfluence(strong,moderate,

weak,andnone)accordingtothedegreeofinfluencethatCMA/CAshaveonthem[60].

Finally,whencreatinggroup‐levelSESindicatorsbasedonageographicboundary,

howthatboundaryisdefinedmayinfluencetheresultsobtained.Asstatedabove,

administrativecensusboundariesarethemostcommonforseveralreasons.Thedatais

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routinelycollectedwithahighdegreeofqualityandisfairlystableovertime.However,

researchershaveimplementedtechniquestocreatenewboundarieswithahighdegreeof

within‐areahomogeneitytherebyreducingthepotentialforwithin‐areaconfounding

[62,65,66].InCanada,whileDAsdonotnecessarilyrepresentexistingneighbourhood

communities[62],theycanserveasadequateproxiesforageneralcatchmentareaof

personalhome‐lifeactivities[63,64].

Underlyingtheboundaryselectionissueistheconceptofspatialscaledependencies

orthe‘modifiablearealunitproblem’(MAUP)[67].MAUPhastwodistinctcomponents:the

scaleeffectandthezoningeffect.Thescaleeffectrecognizesthatasoneaggregatesdataup

thespatialhierarchytherelationshipbetweenvariablescanchangeoftenstrengtheningthe

regressionrelationshipbutreducingstatisticalpowerduetotherebeingfewerunitsinthe

analysis.Thezoningeffectreferstotheactualdelineationofboundariesandhowtheir

manipulationcandrasticallyalterneighbourhoodcharacteristicsduetotheinclusionor

exclusionofcertaindatapoints(i.e.peopleordiseaseevents).Considerationofthespatial

unitusedintheanalysisshouldbebasedontheresearchquestionandtheprocessthatis

attemptingtobemodelled.InthisdissertationweusecensusDAsastheneighbourhood‐

levelunitofanalysis.DAsarethesmallestgeographicalunitforwhichcensusdataare

availableandrepresentneighbourhoodblocksrangingbetween200–800people.TheDA

waschosenpartlyduetoconvenienceinthatitwasthespatialunitthatmatchedtheSES

indexaswellastheotherSEScensusdatasuchaseducationandimmigrantdensity.Butthe

DAwasalsochosenforpracticalreasonsduetothescaleofthisprojectencompassingthe

entireprovinceofBC.Whilescalingupinurbanareascouldhavebeenexplored,thiswould

beimpracticalforthelargergeographicsizesofruralDAs.Managingthreedifferent

geographicspatialunitsorhavingdifferentspatialunitsforurbanandruralareaswouldbe

anadditionalcomplicationwithpossiblyminimalbenefitininterpretation.Furthermore,

usingDAsallowsforthecomparisonofresultstootherstudies.

3.0MultilevelModels&Analysis*muchofthissectionhasbeenadaptedfromSnijdersandBoskar,MultilevelAnalysis:AnIntroductiontoBasicandAdvancedMultilevelModeling[25].

Multilevelmodelsgobymanynamesandhaveseveralflavours.Whilenotanew

methodology,itisonlyinthelasttwodecadesthattheiremergencehasmovedbeyondthe

socialsciences(sociology,education,economics,criminology,etc.)andintopopulation

healthandepidemiology.Multilevelanalysisisamethodologyfordatawithnestedsources

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ofvariability,whereinthebetweengroup(orcluster)variabilityisasimportantaresearch

questionasthewithingroupvariability.Bydecomposingtheoverallvariabilityinto‘within’

and‘between’unitsofanalysis,theresearcherisabletoadjustforandquantifyintra‐group

dependencewhilesimultaneouslyquantifyingtheinter‐groupvariability[25].Forexample

ineducationalresearch,therecanbevariabilitymeasuredatthestudentlevelwith

individualattributes(e.g.IQontestscores).Thesestudentsarenestedwithinschoolsthat

havetheirowncharacteristicsanditispossibletomeasurethebetween‐schoolvariability

acrossdifferentschools(i.e.eachschoolwillimpartsomeuniqueeffectonindividualtest

scoresandtheadditionofschool‐levelvariables(e.g.publicvs.private)areusedtohelp

explainthemeasureddifferences).Additionally,schoolsareoftennestedwithinschool

districtswithdistinctcharacteristics.Dependingontheresearchquestion,allthreeofthese

‘levels’(student‐school‐district)mayhaveinterestingandnecessaryvariabilitytoaccount

for.

Multilevelanalysisasitistodaywasformedbytheconvergenceoftwodistinctfields

ofinquiry;namelycontextualanalysisasadevelopmentinthesocialsciencesrecognizing

theinfluenceofsocialcontextonindividualbehaviour/outcomes,andthedevelopmentof

mixedeffectstatisticalmethodsthatallownestedregression‐typemodelcoefficientstobe

eitherfixedorrandom.Theconfluenceofthesetwostreamscoincidedwiththeemergence

ofthecomputationalprocessingeraintheearly1980swhichallowedgreateraccessibility

inrunningmorecomplicatedmodels[25].

Thehierarchicallinearmodelisthebackboneofmultilevelanalysisandisessentially

anextensionofmultiplelinearregressiontoincludenestedrandomeffects[25].The

employmentofanestedresearchdesignsometimesarisesfromnecessityandothertimes

ascuriosity.Respectfully,thiscanbereferredtoasdependenceasanuisanceordependence

asaninterestingphenomenon.Theformerarisesfrommulti‐stagedsamplingdesignswhich

inherentlyleadtodependentobservations(i.e.selectionofagroupingunit(school)

increasesthelikelihoodintheselectionoftheindividualunit(student)).Thisdependence

insamplingviolatesanessentialassumptionofinferentialstatisticscausingdeflated

standarderrorsandpotentiallyleadingtoTypeIerrors.Inthelattersituation,dependence

betweenandwithinnestingstructuresisthefocalinterestwhereonewishestomake

inferenceatbothlevels.Regardlessofthemotivation,single‐levelstatisticalmodelsareno

longervalidalthoughtherearestatisticalmeanstodealwithnuisanceclusteringof

observations(e.g.sandwichestimatorsofstandarderrors)[25].

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Theterminologyusedtodescribethemultilevelmodelisvaried.Forourpurposes

here,wewillrefertothemacro‐unitsaslevel‐2orgroup‐levelunitsandsignifiedin

equationsasasubscriptj.Themicro‐unitswillrefertolevel‐1orindividual‐levelunitsand

signifiedinequationsasasubscripti.Inthestudyofmultilevelsystems,sixpropositions

canbedistinguishedandarediagramedbelowinFigure7tofacilitatecomprehension.

Figure7:Sixtheoreticalmultilevelproposition

3.1RandomInterceptModel

Therandominterceptmodelisoneinwhichtheinterceptisallowedtovaryrandomly

betweenthelevel‐2groups.Thisallowsforeachlevel‐2group(e.g.neighbourhoods,

schools)tohaveitsowninterceptalongwithitsvariancefromtheoverall(global)intercept.

Whenregular(ordinaryleastsquaresorOLS)regressionisusedondatathathasa

meaningfulhierarchicalstructure,itis(erroneously)assumedthatallthegroupstructureis

representedbytheexplanatoryvariableswhichisreflectedbythesinglefixedintercept.

Therefore,foranytwoindividualsitisinconsequentialwhethertheyarefromthesameor

differentschoolorneighbourhood.

Thesimplestwaytothinkaboutrandominterceptmodels,orhierarchicallinear

modelsingeneral,istoconsideritaspecialcaseofregressionmodelsbutwithmorethan

oneerrorterm.Thegroup‐dependentinterceptcanbesplitintoanaverageinterceptand

thegroup‐dependentdeviationfromtheoverallmean(i.e.level‐2randomerror).This

59

signifiestherealizationthatsomegroupswillhavehigherresponsesYonaverageand

otherswillhavelowerresponses.Theregressionequationbelow(Eq.1)showsthatthe

interceptdependsonthegroup(signifiedbythesubscriptj).Fornowthe coefficientofX

isconstantorfixed,butthenextsectiondescribeshowittoocanbeallowedtovary

betweengroupstoproducetherandomslopemodel.

(Eq.1)

(Eq.2)

(Eq.3)

ReferringtoEq.1,theinterceptfromlevel‐1, ,isestimatedasamodelparameterwithits

owngroup‐levelregressioncoefficient(γ10)anderrorterm(U0j)thatdeviatesfromthe

overallaverageintercept(γ00).Substitutionproducestherandominterceptmodel

presentedasEq.3,sometimesreferredtoasan‘intercept‐as‐coefficientmodel’.

Theemptyornullmodelisamodelthatdoesnotcontainanyexplanatoryvariables.

Here,thedependentvariable( )isonlyafunctionoftheoverallmean(γ00),thegroup‐

levelrandomeffect(U0j),andtheindividual‐levelrandomeffect(Rij).

(Eq.4)

ItisassumedthattherandomvariablesU0jandRijhaveameanof0,aremutually

independent,drawnfromnormallydistributedpopulations,andhavevariancevar( )=σ2

andvar(U0j)=τ02.Thenullmodelprovidesthebasisforcalculatingtheintraclasscorrelation

coefficient(ICC),aparameterthatindicateshowthevariabilityinthedataispartitioned

betweenthetwolevels.TheICCiszerowhenthereisnobetween‐cluster(macro‐unit)

variance,andincreaseswhenthebetween‐clustervarianceincreasesrelativetothewithin‐

clustervariance.RepresentedbytheGreeksymbolrho(ρ),theICCiscalculatedbydividing

thebetweenpopulationvarianceofthemacro‐unitsbythesumofthemacro‐andmicro‐

unitvariance,τ2/(τ2+σ2).TheICCcanbeinterpretedintwoways:1)itisameasureofthe

correlationbetweentworandomlydrawnmicro‐unitsbelongingtothesamemacro‐unit

drawnatrandom;2)itistheproportionofvarianceaccountedforbythegroup‐level.

Fromthenullmodelwebegintointroduceexplanatoryvariablesinordertoexplain

thevariabilityofYatbothlevel‐1andlevel‐2.ReturningtoEq.3,thismodelhasfour

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parameters:thetworegressioncoefficientsγ00andγ10whichmakeupthefixedeffectsand

thetwovariancecomponentsσ2andτ02(therandomeffects).ThevaluesU0jarethemain

group‐leveleffects(orgroupresiduals)thatareleftunexplainedbyX.Theoverallintercept

γ00isthefixedinterceptfortheaveragegroup(averagedoverallgroupsbutwithlarger

groupshavingmoreinfluenceonitsestimationthansmallgroups).Thefixedregression

coefficientγ10isinterpretedintheusualway(i.e.aone‐unitincreaseinXisassociatedwith

anaverageincreaseinYofγ10units).

Theadditionoflevel‐2explanatoryvariables,denotedbyZ1…Zq,takestheforminone

oftwoways.Level‐2variablescanbedirectlydefinedbyaunitcharacteristic(e.g.thelevel‐

2groupofschoolscanbeeitherprivateorpublic).Alternatively,alevel‐2variablecanbe

definedbytheaggregatedunitsatlevel‐1(e.g.themeanSESofstudentsinaschool).This

lattercharacterizationbasedongroupmeansisaparticularlyimportanttypeof

explanatoryvariableasitallowsforthedifferentiationbetweenwithin‐groupandbetween‐

groupregressionstobeexpressed.Thewithin‐groupregressioncoefficientexpressesthe

expecteddifferenceinYbetweencaseswithinthesamegroupforaone‐unitdifferencein

theexplanatoryvariableX;whereasthebetween‐groupregressioncoefficientisthe

expecteddifferenceinthegroupmeansonYbetweengroupsforaone‐unitdifferencein

theirmeanvaluesofX.Thisisnoteworthybecausetheprocessesatworkwithingroupscan

differfromtheprocessesatworkbetweengroups,andcouldthereforehavevastlydifferent

conceptualinterpretations.Forexample,theeffectofahighproportionofneighbourhood‐

levelmaternalsmokingonbirthweighthasadifferentconceptualinterpretationthanthat

ofindividual‐levelmaternalsmokingonbirthweight.

3.2RandomSlopeModel

Inadditiontogroupsdifferingwithrespecttotheaveragevalueofthedependent

variable(i.e.randombetween‐groupinterceptvariance),itispossiblefortheeffectofan

explanatoryvariabletoalsodifferbetweengroups(i.e.randombetween‐groupslope

variance).Thisisreferredtoasheterogeneityofregressionsacrossgroupsorasgroup‐by‐

covariateinteractionintheanalysisofcovariance(ANCOVA)terminology.Returningto

Eq.1,amodelwithgroup‐specificregressionsofYonalevel‐1variableX,boththeintercept

andtheregressioncoefficients,orslopes, canbesplitintoanaveragecoefficientand

thegroup‐dependentdeviationtoproduceEq.2(asseenaboveintherandomintercept

model)aswellasEq.5.SubstitutionleadstothemodelinEq.6

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(Eq.1)

(Eq.2)

(Eq.5)

(Eq.6)

Againhere, istheoverallaverageinterceptand istheoverallaverage

regressioncoefficientandarereferredtoasthefixedeffects.Thelevel‐2residuals, and

,alongwiththelevel‐1residual arereferredtoasrandomeffectsandareassumedto

haveameanof0giventhevaluesoftheexplanatoryvariableX.Theterm isregarded

asarandominteractionbetweengroupandX;thatis,theeffectofalevel‐1attributecan

differbetweengroups.Therandominterceptandrandomslopeareconsideredlatent

variables,astheyarenotdirectlyobservedbutareinfluentialinproducingtheobserved

variables.TheterminologyisthatXhasarandomslope,orarandomcoefficient,ora

randomeffect.Therandominterceptandslopeareoftencorrelated;however,model

assumptionspresumethemtobeindependentandidenticallydistributedbetweengroups

andthattheyareindependentofthelevel‐1residual .Also,itisassumedthatall are

independentandidenticallydistributed.Thevarianceandcovariancesofthelevel‐2

residualsaredenotedas:

var( )=τ00=τ02

var( )τ11=τ12

cov , )=τ01

Therandomslopemodelimpliesheteroscedasticity(unequalvariance)inthatthe

correlationofindividualswithinthesamegroupaswellasthevarianceofYisdependenton

thevalueofX.Forexample,studentsfromlowSEShouseholdsmaybenefitmuchmorefrom

awell‐resourcedschoolcomparedtostudentsfromhighSEShouseholds.Thereforethe

schooladdsacomponentofvariance,butprimarilyforthelowSESstudentsindicatinghow

thevariancedependsonthevalueofXforstudent‐levelSES.

3.3InclusionofLevel‐2VariablesandCross‐LevelInteractions

Theaimofregressionanalysisistoexplainvariabilityinthedependentvariable.For

multilevelregression,thereisnotonlyvariabilitybetweenindividuals(level‐1residual, )

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butalsovariabilitybetweengroupstowhichtheindividualsbelong(level‐2random

intercept, )andvariabilitybetweengroupsinhowtheyinfluence/interactwith

individual‐levelattributes(level‐2randomslope, ).Eachrepresentdifferentpartsofthe

unexplainedvariability,andthuseachcanbethepointoffocuswhenconsidering

explanatoryvariables.Theadditionofindividual,orlevel‐1,variablesaimtoreducethe

level‐1residualvariance(σ2).Theirinclusionmayalsoreducetheresidualvarianceatlevel‐

2giventhatgroupscandifferwithrespecttotheircompositionoflevel‐1variables.The

additionofgroup,orlevel‐2,variablesaimtoreducethelevel‐2randominterceptresidual

variance(τ02).Thisleadstoanexpandedmodelinwhichthegroup‐dependentregression

coefficients and fromEq.2andEq.5nowincludealevel‐2variableZtoproduceEq.7

andEq.8.Substitutionbackintothebasicmodel(Eq.1)leadstothemodelinEq.9.

(Eq.7)

(Eq.8)

(Eq.9)

Theadditionofthelevel‐2variableZleadstoamaineffectofZ( aswellasa

cross‐levelinteractioneffectofXandZ( .Thecross‐levelinteractioneffectisthe

productbetweenalevel‐1andlevel‐2variablethataimstoreducethelevel‐2randomslope

residualvariance(τ12).Inordertohelptheinterpretationofcross‐levelinteractions,itis

stronglyadvisedtogrand‐meancenterthecomponentvariablesXandZsothatthevalueof

0hasaninterpretablemeaning(e.g.theaveragestudentandschoolSESrespectively)or

thatitcorrespondstoacommonreferencevalue(e.g.malestudents,publicschools).

Theapplicationofcross‐levelinteractionscanbeapproachedeitherinductivelyor

theoretically.Theformerimpliesapost‐hocapproachoftestinglevel‐2variablesbasedon

thepresenceofasignificantrandomslope.Thelatterimpliestheiraprioriinclusionbased

onsubstantiveortheoreticalarguments,regardlessofwhetherarandomslopewasfound.

Thedecisionforwhichvariablestogiverandomslopes,cross‐levelandwithin‐level

interactionswilldependonthesubject‐matteraswellastheoreticalandempirical

considerations.Havingasufficientsamplesizeisimportant,butespeciallyatthegroup‐

level.Includingmanyrandomslopesincreasesthedegreesoffreedomaswellasthe

difficultyinmodelinterpretation.Parsimonyshouldalwaysbeadrivingfactorinmodel

specification.

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3.4MultilevelLogisticRegression

Thepurposeoflogisticregressionistopredicttheprobability(pi)thatsomeevent

occursforanindividual(i)conditionalonanumberofvariables.Duetothenatural

constraintsoftheprobabilitydistributionfallingbetween0and1,piistransformedusinga

logitfunctionwhichgivesitadistributionofvaluesbettersuitedforregressionanalysis,

namely‐∞and+∞.Similartomultilevellinearregression,multilevellogisticregression

considersindividualstobestatisticallydependentontheirareaofresidenceorsome

hierarchicalarealunitforwhichtheindividualsarenestedwithin.Itisthismodeled

dependenceandtheabilitytopartitionitsvarianceatthedifferentlevelsthatisthemodus

operandiofmultilevelregressionrelevantforbothstatisticalandsubstantiveepidemiologic

reasons(i.e.improvedestimationandimprovedcontextualunderstandingofarea‐level

measuresonindividualoutcomes).However,whileforthelinearcasethisdependencecan

bequantifiedusingmeasuressuchastheintraclasscorrelation(ICC)discussedabove;the

componentsofvarianceinlogisticmodelsaremorecomplicatedduetothenonlinear

relationbetweenthecovariatesanddichotomousresponsevariable.Asaresult,theICChas

statisticalandinterpretationaldrawbacks[68,69].

Twoalternativemeasuresofinterpretationhavethereforebeenproposed.These

includethemedianoddsratio(MOR)andtheintervaloddsratio(IOR)[68–70].TheMOR

dependsdirectlyonthearea‐levelvarianceandisdefinedasthemedianoddsratiobetween

anytwoareaspickedatrandomwithdifferingrisk(i.e.whatisthemedianincreaseinrisk

foranindividualmovingtoanareawithahigherrisk).TheaimoftheMORistotranslate

thearea‐levelvariancetothemorewidelyunderstoododdsratioscalewhichpermitsthe

directcomparisonofitsmagnitudetothatofthelevel‐1andlevel‐2factors.AnMORequal

toone,orforwhichits95%confidenceintervals(95%CIs)overlapone,wouldindicateno

between‐areadifferencesintheindividual‐levelprobabilityoftheoutcome.AnMORand

95%CIsgreaterthanonewouldindicatebetween‐areaheterogeneityandthattheareaof

residenceisimportantforunderstandingthevariationsintheindividualprobabilityofthe

outcome.Thiswouldtheninvitetheintroductionofarea‐levelvariablestotestwhether

theycanexplainanyofthebetween‐areavariability[68,69].

Theintervaloddsratio(IOR)isafixed‐effectsmeasureforthelevel‐2variablesthat

takesthearea‐levelvarianceintoaccountwheninterpretingtheirassociationwiththe

outcome.TheIORisanintervalforoddsratiosbetweentwopersonswithsimilarcovariate

patterns,coveringthemiddle80%oftheoddsratios;howeversmallerorlargerintervals

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couldbeselected(e.g.70%or90%).TheIORmakesitpossibletodeterminewhethera

level‐2variableisusefultoidentifyhighriskareas,orwhethertheremainingbetween‐area

variabilityistoolargeforthatvariabletomakeadifference.Forexample,iftheIORfora

level‐2variabledoesnotcontainone,thenthissuggeststhatitseffectislargerelativetothe

area‐levelvariance.Conversely,iftheIORforalevel‐2variabledoescontainone,thenthat

variabledoesnotaccountforasubstantialamountofthearea‐levelvariability.Thewidthof

theIOR,whetherit’snarroworwide,givesanindicationoftheamountofunexplained

between‐areavariationinthepropensityofanindividualtohavetheoutcome.Thus,while

related,theMORandIORprovidedifferentinformationusefulintheanalysisofcontextual‐

leveleffects[68,69].

Inthemultilevelframework,observationswithinaparticulargeographicalareaare

assumedtobestatisticallyindependentofthosefromanotherarea(regardlessof

adjacency)therebyignoringanyspatialassociationsbetweenthegeographicalmacro‐unit

areas[71,72].Therefore,themultilevelframeworkhasbeencombinedwithspatial

regressiontechniquesinordertoaccountforthepotentialofspatialdependency(i.e.spatial

autocorrelation)ofthearea‐levelresiduals[73].Thefollowingsectionprovidesabrief

reviewofthemechanismsandmethodsofspatialdependenceinhealthdata.

4.0SpatialDependence:Mechanisms,MethodsandModelsSpatial(inter)dependenceisubiquitous.AsstatedinTobler’sso‐called1stLawof

Geography:everythingisrelatedtoeverythingelse,butnearthingsaremorerelatedthan

distantthings.Whilesimplistic,thisinterpretationofspatialinterdependenceasitrelatesto

theinteractionbetweenthephysicalandsocialenvironmentsandreproductivehealthis

appropriatetoformthebasisofbuildingempiricalmodels.Inpracticehowever,

accommodatingspatialdependenceintostatisticalmodelsisacomplexendeavour,whichis

whyitisoftenignoredorarguedasirrelevant.Itwasrecognizedover120yearsagobySir

FrancisGaltonthatdrawinginferencesfromsamplesthatarenotstatisticallyindependent

canyieldmisleadingconclusions,astatisticalphenomenonnowknownas

(spatial/temporal)autocorrelation[74].Severalmethodsandmodelshavebeendeveloped

capableofaccountingforspatialeffects(i.e.spatialdependenceand/orspatial

heterogeneity)[75,76],inwhichthefollowingparagraphswillbrieflydescribeasameans

tointroducethemethodologiesandterminologyusedthroughoutthisdissertation.

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4.1SpatialDependence&SpatialHeterogeneity

Ingeneral,spatialdependenceexistswhenthevalueofavariableatonelocationis

dependentonitsvaluesatotherlocations.Itcanariseduetospatialinteractioneffects(e.g.

externalitiesorspill‐overeffects[73,77]),orfrommeasurementerror(e.g.scalemismatch

betweenhowavariablewasmeasuredandthatatwhichitoccurs[78,79]).Spatial

autocorrelationisanindicatorofspatialdependenceinwhichavariableiscorrelatedwith

itselfinspace.Spatialheterogeneity(ornon‐stationarity)ontheotherhandrefersto

structuralchangesinadatasetrelatedtolocation,suchasdifferencesinthemean,variance,

and/orcovariancestructureofavariableacrossspace(e.g.therelativedifferenceofSES

differbetweenurbanandruralregimes[80]).Ignoringtheseaspectsofthedatacanleadto

inefficientorbiasedestimatesresultinginmisleadinginference.Forexample,thepresence

ofpositivespatialautocorrelation,wherehighorlowvaluescorrelatewithhighorlow

neighbouringvalues,resultsinalossofinformationinthecontextofgreateruncertainty,

lessprecision,andlargerstandarderrors.Thepresenceofspatialheterogeneitycanresult

fromheteroscedasticity(non‐constanterrorvariance)acrossspaceandindicatescale‐

relatedmeasurementerrors[78,79].Inpractice,itcanbedifficulttodistinguishbetween

spatialdependenceandspatialheterogeneity,andtestsforonemaysignaltheotherand

viceversa[81,82].

4.2SpatialWeightMatrices

Inspatialanalyses,theconceptofspaceisoperationalizedthroughtheuseofspatial

weightsmatrices.Definedastheformalexpressionofspatialdependencebetween

observations[75],theycontaininformationregardingtheneighborhoodorconnectivity

structureinthedatainordertoassesstheextentofsimilaritybetweenlocationsandvalues

(e.g.spatialautocorrelation).Theresearchermustmakeanassumptionregardingthe

structureofinterdependencebetweenanytwoobservations.Thisiscommonlybasedon

adjacency/contiguity(i.e.sharingacommonboundaryoraspecifiednumberofnearest

neighbours)orproximity(i.e.numberofneighbourswithinagivendistance);however,

morecomplexformalizationsarepossiblesuchasusingafunctionaldistancebasedon

traveltimeasopposedtoEuclidian(straight‐line)distance,addingadistance‐decay

mechanismorevensomenon‐geographicnotionofdistance[83].Inshort,theselectionof

thespatialweightmatrixshouldbebasedonsomesubstantiveknowledgeaboutthespatial

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processbeingmodeledandsensitivityanalysesusingdifferentweightmatricescanbe

appliedtotesttheconnectivityassumptions.

Ingeneraltherearetwobasiccategoriesofweightmatrices:contiguity‐based(i.e.

sharedborders)anddistance‐based.Examplesofcontiguity‐basedweightmatricesinclude

RookandQueencriterionwhichsymbolizetheselectionofneighbourstothemovementof

thecorrespondingchesspieces(e.g.north‐south/east‐westforRook,andallpossible

directionalverticesforQueen).Distance‐basedweightmatricescanincludeafixeddistance

criterionbutcouldincludemorenuancednotionsofdistancesuchastime‐travelled

distances,perceptivedistances,orpolicy‐defineddistance[83].Finally,knearest

neighbours(KNN)isatypeofdistance‐basedweightmatrixwhere“k”referstoaspecified

numberofneighboursforeachobservationalunit.Itusesthedistancebetweenthecentral

points(orcentroids)ofthearealunitpolygonstodeterminethenearestneighbouringunits.

Itisusefulinsituationswherethearealunitsareofvaryingsizetoensurethatevery

locationhasanequalnumberofneighboursaswellasinsituationswheresomearealunits

are“islands”(i.e.havenocontiguousneighbours).KNNweightmatricesareasymmetric,

thatis,areaAisB’snearestneighbourbutnotnecessarilyvisa‐versa;whereasQueenand

Rookcriteriaaresymmetric.Thiscanhaveimplicationsonthetypesofspatialanalysesthat

canbeperformed[82].

Thespatialweightmatrixismadeupofonerowandcolumnforeachobservational

unitusingbinary(0,1)codestodefineneighboursas1andnon‐neighboursandthefocal

locationas0(Figure8).Neighboursofneighbourscanbedefinedbyhigherordersof

contiguity.Row‐standardizationtransformstheweightmatrixsothatthevaluesineach

rowsumstoone.Row‐standardizedweightmatricesareusefulwhenwantingtocompare

differentmodelcoefficients.

Thereisnorealempiricalmethodthatcanbeappliedtoselectthe“correct”spatial

weightmatrix;however,twoconsiderationsshouldbetakenintoaccount.Thefirstis

theoreticalandrelatestothehypothesizedunderlyingspatialprocessthatisbeing

Figure8:n byn binaryspatialweightmatrix

Elementswij representingtheassociationbetweenregionsiandjwherewij=1ifiandjareneighbours,wij=0otherwise.

67

assessed.Theotherismoretechnical,andhastodowiththetessellationofareal(polygon)

unitused,beitirregularshapedadministrativeareas(e.g.censusareas),auniformgrid(e.g.

5x5kmgrid),orsomeotherlattice(e.g.Thiessenpolygons).

Somesuggestselectingthespatialweightmatrixbasedonthestrengthofthespatial

autocorrelationtest(e.g.Moran’sI).Figure9shows6Morans’Iscatterplotsshowingthe

degreeofclusteringofamodel’sresidualsusingdifferentk‐nearestneighbour(KNN)

spatialweightmatrices.UsingtheabovecriteriaoflargestMoran’sItest,usingaKNN=6

producesthehighestMoran’sI.

Figure9:Moran’sIscatterplotsshowingthedegreeofclusteringofmodelresidualsusingdifferentKNNspatialweightmatrices.

Thereareseveraltestsofspatialautocorrelation.Ingeneral,theymeasurethe

tendencyforhighorlowvaluestoclusterorcorrelatemorecloselyinspacewithotherhigh

orlowvaluesthanwouldbeexpectedifthedatawererandomlydistributed.Globaltestsfor

spatialautocorrelationmeasurethistendencyacrosstheentirestudyarea,whilelocal

indicatorsofspatialassociation(LISA)identifylocal“hotspot”clustersofhighorlowvalues

betweenarealunits[84],ordeterminethedistancebeyondwhichnodiscernablespatial

autocorrelationismeasured[85].Forexample,theMoran’sIstatisticmeasuresthedegree

oflinearassociationbetweenanattribute(y)atagivenlocationanditsweightedaverageat

Toprow,lefttoright:KNN‐4:I=0.0450;KNN‐6:I=0.0480;KNN‐8:I=0.0472;Bottomrow,lefttoright:KNN‐10:I=0.0443;KNN‐15:I=0.0399;KNN‐20:I=0.0384.

68

neighbouringlocations(Wy).Itcanbegraphicallydisplayedusingscatterplots(Figure9)

and/orashotspotclustermaps(Figure10)[84].

Figure10:Moran’sIclustermapshowstheclusteringofmodelresidualsbetweenthearealunits(DAs)usinga6‐KNNspatialweightmatrix.

4.3SpatialRegressionModels

Thespatiallagmodelisalinearregressionmodelthatexplicitlyincorporatesspatial

dependenceintoitsframeworkbyaddinga“spatiallylagged”dependentvariableasan

independentvariableontheright‐handsideoftheregressionequation.Sometimesalso

calledaspatialautoregressivemodel,thismodelisappropriatewhenitisbelievedthatthe

valuesofthedependentvariableinthefocalneighbourhoodisdirectlyinfluencedbythe

valueofthedependentvariableinneighbouringunitsaboveandbeyondthepresenceof

othercovariates.Alternatively,ifitisbelievedthatalatentspatiallyclusteredprocessor

variableisinfluencingthedependentvariableinthefocalandneighbouringunits,thenthe

spatialerrormodelmaybemoreappropriate[86].

Asignificantspatiallagtermmayindicatestrongspatialdependence,butmayalso

indicatethepresenceofMAUPregardingthemismatchofspatialscalesbetweenthespatial

processunderstudyanditsmeasurementbysomeproxyvariable.Asignificantspatial

69

errortermindicatesspatialautocorrelationintheerrors,likelyduetothepresenceof

importantunmeasuredexplanatoryvariablesnotincludedinthemodel[81].

Thus,forexample,letthedependentvariablebetheprevalenceofmaternal

smokingwhilepregnantinaneighbourhood.Itcouldbethatthisispartlyafunctionof

maternalsmokinginadjacentneighbourhoods,ratherthanjustbeingrelatedtocommon

unmeasuredvariablesinthoseneighbours.Hencethespatiallagmodelmightofferinsight

intospatialexternalitiesofthespatialtransmissionof(un)healthybehaviours,andimplies

thatthespatialdependenceisaninterestingphenomenontoinvestigate.Thespatialerror

modelontheotherhandimpliesthatthepatternofspatialdependenceisattributableto

unmeasuredcovariates(i.e.thestochasticcomponents)only.Hence,spatialerrormodels

arerarelytheory‐driven,andemployedmainlytoaccommodatenuisancespatial

dependence[83,86].

4.4RateSmoothing

Ratesmoothingisonewaytoaddressthevarianceinstabilityofrawratesin

populationsofdifferentsizes.Smoothedratesarestabilizedbyborrowingstrengthfrom

neighbouringspatialunitsinwhichtheamountofsmoothingisinverselyproportionalto

theunderlyingsizeofthepopulationatrisk.Thatis,areaswithsmallpopulationcountswill

producerawriskrateswithlargestandarderrorsandwillthereforeundergogreater

smoothingadjustments.Thereareseveraldifferentflavoursofratesmoothers[87].

Excessriskmaps(akastandardmortalityratesorSMRs)areusedtovisualizethe

degreetowhicharealunitratesexceedorarebelowtheexpectedaveragerisk.Excessrisk

mapsarebasedontheratiobetweentheobservedtoexpectedcountsofanevent(SMR=

Observed/Expected).Expectedcountsaretheproductofthelocalriskrateandtheaverage

overallriskforthestudyregion.Unlikesmoothers,excessriskmapsonlyre‐scaletheraw

ratesothatthemagnitude,nottheorder,oftheoriginaldatachanges.Thesemapshowever

donotprovideinformationonstatisticalsignificanceoftherates[82,87].

TheSpatialRatesmootherissimilartoaspatialmovingaverageinwhichrawrates

arecomputedforeacharealunitaswellasitsneighboursasdefinedbythechosenweight

matrix.It’sapplicationisusefultoidentifyspatialregimesinthedata,aformofspatial

heterogeneitywhichimpliesstructuraldifferencesacrossspace[82,87].

TheEmpiricalBayes(EB)smootherisbasedontherawrateforeacharealunitthatis

averagedusingapriorestimatebasedontheentirestudyregion(i.e.theoverall(global)

populationmeanandvariance).Inlargestudyareaswhereregionalcharacteristicsmay

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influencetherates,alocalpriorcanbesubstitutedovertheglobaloneasdefinedbyagiven

spatialweightsmatrix(socalledthespatialEBsmoother).Whenselectingbetween

smoothers,considerationshouldbegiventotheareawherestrengthisborrowedfromand

itsrepresentativenessoftheunderlyingriskinthelocalarea[82,87].

Theadvantageofsmoothingcanbeadouble‐edgedsword.Thebenefitsincludethe

removalofspuriousoutliersandtheabilitytorepresentoverallpatternsbeyond

particularlyhighandlowvalues.Theflipsideisthatresultscanbequitesensitivetoagiven

smoother,andover‐smoothingmayconcealinterestingextremecases.Itiswisetoperform

aseriesofsensitivityanalysesusingdifferentsmootherstogaugethedegreeofchangein

theresults.EBstandardizationprocedureisusedintheglobalMoran’sIscatterplotand

LISAmaps.Itdirectlystandardizestherawratestoobtainaconstantvariancewithazero

meanandstandarddeviationofone.

5.0PerinatalDataRegistriesandAdversePregnancyOutcomes

5.1TheBCPerinatalDatabaseRegistry

TheuseofexistingbirthregistriestoidentifyenvironmentandSES‐relatedhealth

relationshipsisausefultoolinpublichealthmonitoringandsurveillanceresearchwhich

hasbeeninpracticefordecadesinScandinaviancountries[27,88–91].InCanada,the

CanadianPerinatalSurveillanceSystem(CPSS)isapartnershipbetweenseveralfederal

agenciestogetherwiththeprovincesandterritorieswhichareresponsibleforcollecting

specificindicatorvariablesandsharingthemwiththeCPSSfornationalassessmentand

interpretation[92,93].Whilenationalsurveillanceisimportant,theresolutionofthedata

collectedbytheCPSSistoolowforresearchintoputativeenvironmentalexposuresand

accessingprovincialregistrydataisoftenbettersuitedforthistask.

TheBritishColumbiaPerinatalDatabaseRegistry(BCPDR)managedbyPerinatal

ServicesBC(PSBC)isacomprehensive,province‐wideperinataldatabasecollectedforthe

purposeofevaluatingperinataloutcomes,careprocessesandresources,ultimately

improvingmaternal,fetal,andnewborncare.TheRegistryaccountsfor99%ofbirthsinBC

collectedfromfacilitiesthroughouttheprovinceonavoluntarybasis.Datacollected

includes:antenatal,intrapartumandpostpartummaternalandinfantcareandoutcomes,as

wellasneonatalfollow‐upandoutcomesincludinglinkingtoBCVitalStatstoprovideinfant

deathdata.Dataqualityisaddressedbyvalidationedits,errorsandwarningsaspartofthe

dataentrysoftwareprogram,periodendchecksandreports[94].Thirdpartydataaccessis

providedbyaPartnershipAccord/MemorandumofAgreementbetweenallBCHealth

71

AuthoritiesandPSBCthroughtheFreedomofInformationandPrivacyProtectionAct.Data

releaseapplicationsarereviewedbytheResearchReviewCommitteewithrepresentation

fromhealthcareproviders,healthauthorities,andacademicorganizations[95].One

shortcomingoftheBCPDR,aswellasmanybirthregistriesinCanada,isthehandlingof

congenitalanomalysurveillance.Thecompleteandreliableascertainmentofcongenital

anomaliescanbethemostchallengingaspectofmaintainingahighqualitysurveillance

system.Thereforedespitehavingthedata,congenitalanomaliesarenotassessedinthis

dissertation.

5.2AdversePregnancyOutcomes

5.2.1MeasuresofBirthWeight

WhenconsideringmeasuresofbirthweightinstudiesofAPOs,itisimportantto

differentiatebetweenthreerelatedtermssometimesusedinterchangeablybutwhichare

notnecessarilysynonymous.Lowbirthweight(LBW)referstoinfantsbornweighingless

than2,500gramsregardlessofgestationalage,althoughdistinctionsbetweenpreterm,

termandpost‐termLBWcanbemade[96].LBWistheresultoftwooverlappingbut

distinctetiologies,PTBand/orIUGR,thedeterminantsandhealthconsequencesofwhich

arequitedifferent[17].Therefore,theuseofLBWinepidemiologicalstudiescanbe

problematicduetheambiguityofitsunderlyingetiology.Small‐for‐gestationalage(SGA)

referstoinfantswhoseweightfallsbelowathresholdcut‐pointofagivenpercentile(e.g.

the10thor3rd)usingsex‐specificbirthweightforgestationalagedistributioncurves[97],

althoughfurthercustomizationbasedonmaternalcharacteristicshavebeenargued[43–

45].Intrauterinegrowthrestriction(IUGR,orfetalgrowthrestrictionFGR)isdefinedasthe

processthatleadstoSGAorLBWbysomeaetiologythatlimitsthegrowthofthefetusfrom

itsfullpotential[98,99].Itisdiagnosedduringtheantenatalperiodfrommeasurementsof

fetalgrowthusingultrasoundtechniquesandplottingagainststandardizedfetalgrowth

curvesforthegivengestationalage[100].Ifmeasurementsandweightfallbelowthetenth

percentile,fetalgrowthrestrictionissuspectedandtherapeuticmanagementcanbe

initiated.ThedeterminationofwhethertheFGRisasymmetricalorsymmetricalgivesclues

tothecausalmechanism.SymmetricFGRdescribesafetusthatisproportionallysmalland

mayjustbeitsconstitutionalmake‐up;conversely,asymmetricalFGRindicatesthatafetus

isundernourishedandisdirectingitsenergyawayfromtheliver,muscleandfatto

72

prioritizegrowthofvitalorganssuchasthebrainandheart.AsymmetricFGRisindicative

ofplacentalinsufficiencyandisassociatedwithworseperinataloutcomes[99].

5.2.2PretermBirth

Pretermbirth(PTB,deliveriespriorto37completedweeksofgestation)is

consideredasoneofthemostimportantperinatalchallengesfacingindustrializedcountries

duetohighinfantmortality,seriouslong‐termmorbiditiesandelevatedhealthcarecosts

[101].Clinically,PTBiscategorizedaseither1)spontaneouspretermlabourwitheither

intactmembranesorpretermprematureruptureofmembranes(PPROM);or2)medically

indicated(iatrogenic)formaternalorfetalconditionssuchaspreeclampsia,haemorrhage,

non‐reassuringheartrateandIUGRinwhichlabouriseitherinducedorthefetusis

deliveredbypre‐labourCaesareansection[102].

Incidenceratesinmanyindustrializedcountriesarerising,largelyduetoincreasesin

medicallyindicateddeterminantssuchaslabourinductionandprelabourcaesarean

deliverybutalsoattributedtoariseintwin/multiplebirthsandbetterestimatesof

gestationalageviaultrasound[102,103].Whendividedintoearly(<31weeks),moderate

(32‐33weeks)andmild(34‐36)PTB,thelargestrelativeandabsoluteincreasesareinthe

lattercategorywhichalsoaccountforthelargestproportionofPTBsinCanadaandthe

UnitedStates.Therefore,whiletherelativerisk(RR)ofinfantdeathamongmildPTBis

smallercomparedtoearlierPTBcategories,etiologicfractionofmildPTBininfantdeath

hasalargerpopulation‐levelimpactduetoitsmuchhigherincidence[104].

Theratesofspontaneouspretermparturition,eitherwithintactorruptured

membranes(PPROM),havebeenstableoverthepastdecadebutaccountsforoverhalfof

PTBcases[102].Theproposedmechanismsthatinitiatethissyndromeincludefactorsthat

stimulatetheinflammationpathwaysuchasinfection,stress,vasculopathicischemiaand

otherimmunologicalandendocrinedisorders[105–107].Meanwhile,theobservedincrease

iniatrogenicPTBsreflectstheincreaseinmildPTBandprelabourcaesareandelivery.An

analysisinCanadashowedthatthelargestindicationandoverallincreaseinprelabour

caesareandeliverywasduetopregnancyinducedhypertension/preeclampsia(PIH/PE)

andpoorfetalgrowth[108].Thereforeregardlessofwhetherit’sspontaneousoriatrogenic

PTB,bothareassociatedwithanddisplaypathoetiologicmechanismsrelatedtoair

pollutionexposure[109–111].

73

5.2.3PregnancyInducedHypertension&Preeclampsia

Preeclampsia(PE)isamultisystempregnancydisorderuniquetohumansthataffects

between2to7percentofpregnanciesandischaracterizedbysystemicendothelial

dysfunctionandincreasedvascularresistance,plateletaggregationandcoagulation

[112,113].Clinicalfeaturesincludenew‐onset(i.e.pregnancyinduced)hypertension(PIH)

andproteinuriaafter20weeksgestation;however,itspathogenesisisthoughttostart

shortlyafterimplantationwithimpairedofnormalplacentationleadingtoinsufficient

placentalperfusionthateventually,dependingonseverity,cascadesintothematernalPE

syndrome[114].Immunemaladaptation,inflammatoryresponseandoxidativestressare

consideredkeymechanisms,whichisevidencedbymanyofthematernalriskfactors.This

isincludesbeingnulliparousorhavinglimitedexposurewithapartnerpriortoconception

(primipaternity),bothofwhichgivescluestoanimmunologicalpathogenesisandmay

explainthehigherriskinyoungwomenunder20yearsold[112,113].Ontheotherhand,

PIH/PEsharemanyriskfactorsandpathophysiologicalabnormalitiestothoseof

cardiovasculardisease(CVD).Thisincludeschronichypertension,diabetesmellitus,obesity

andadvancedmaternalage,allofwhichareassociatedwithexcessoxidativestress[114–

116].Interestingly,smokingisprotectiveagainstthedevelopmentofPIH/PE;howeverthis

protectionmaybelimitedtosmokinginthethirdtrimester[117].Smokingmayexertits

protectiveeffectbyincreasingVEGFAexpressionintheearlystagesofpregnancy[118],

and/orbyreducinglevelsofVEGFantagonistssuchassolubleFlt‐1(sFlt‐1)andsoluble

endoglin(sEng)releasedbyplacentalvilliincompromisedplacentas[117].Thisproposed

mechanismmayalsotieinwithHO‐1statusandtheincreasedpresenceofCOfromsmoking

asascavengerofsFltandsEng[119,120].Todate,deliveryoftheplacentaistheonly

knowntreatment;thereforePIH/PEisamajorriskfactorforPTBandIUGRanduntil

recentlyhasbeenlargelyneglectedasapotentialmediatingoutcomeonthecausalpathway

betweenairpollutionandAPOs[110].

5.2.4GestationalDiabetes

Gestationaldiabetesmellitus(GDM)isaconditionsthataffectbetween3‐6%of

pregnancies,withratesincreasinginCanada[121,122].GDMisassociatedwithincreased

riskofmaternalandfetal/infantmorbiditiesandmortality[121].Womenwhodevelop

GDMhaveanincreasedlifetimeriskofdevelopingtype2diabeteslaterinlife[123,124].

Riskfactorsincludeincreasedbodymassindex(BMI),familyhistory,andoldermaternal

age[124].Recently,thereisaccumulatingevidencesupportingtheirassociationwith

74

exposuretoairpollution[125–127];therefore,GDMshouldnowbeconsideredamediating

outcomeonthecausalpathwaybetweenairpollutionandotherAPOsco‐relatedwithair

pollution.

6.0ExposureAssessment

6.1ExposureAssessmentTerminology

Themeaningoftheterms‘environment’,‘risk’and‘exposure’candifferamong

differentdisciplines;thisisparticularlytrueforinterdisciplinaryfieldssuchas

epidemiology.Theuseofstandardizedandconsistentlanguageiscriticaltoeffective

communication;thereforesomekeytermsrequiretobeexplicitlydefinedatthistime.The

termagentwillbedefinedasanychemical(e.g.particulatematter),biological(e.g.black

mold),physical(e.g.noise,heat)orpsychosocialstressor(e.g.poverty,violence,housing

quality)towhichanindividualorpopulationisexposed[128,129].Exposureisthenthe

contactofanagentattheinterfacebetweenhumansandtheirenvironmentforsomeperiod

oftime[130].Quantitatively,thiscouldbeachemicalconcentrationinacarriermedium

(e.g.air,water,soil,food),decibels,yearsofeducation;however,theexposurecouldalsobe

assessedqualitativelyorsemi‐quantitativelysuchaspresent/absence,Likertorrelative

scale(e.g.high,medium,orlow).

Inorderforexposureanddosevaluestobeofmuchbenefit,theyneedtoberelated

intointerpretabledescriptorsofrisk.Riskisdefinedastheprobabilityofanadverseeffect

occurring.Thereareavarietyofwaystomodelthelinearornon‐linearrelationshipof

exposuretoindividualorpopulationrisk[131].Inriskassessment,ariskmanagermaybe

interestedintermsofahypothesized‘maximallyexposedindividual’toserveastheupper

limitofariskprobabilityorintermsofhypothesizedscenarios(e.g.lifetimeriskofcancer

forahomeowner2kmfromanoilrefinery).Inobservationalepidemiologyhowever,

obtainingpopulationriskisoftenthegoal(e.g.whatistheprobabilityofadditionalcasesof

diseaseperunitincreaseinexposure)conditionalonage,ethnicity,lifestyle,SES,etc.

6.2ExposureAssessmentQuantification

Thequantificationofexposurecanbeapproachedfromthreedistinctand

independentmethodswhichwillinvariablydictatethetypeofexposuremodeltobe

employed.Theyinclude:point‐of‐contact,reconstructionviatheuseofbiomarkers,and

scenarioevaluation.Eachreflectdifferentapproachestodatameasurementandapplication

(i.e.directorindirectmeasureofexposureordose)withitsownstrengthsandweaknesses.

75

Theapproachtakenultimatelydependsonthepurposeoftheexposureassessmentswell

astheavailabilityofresourcesandqualitydata.

Withrespecttoobservationalorenvironmentalepidemiologicstudies,oftenthe

mainpurposeoftheexposureassessmentistoestablishanexposure‐incidenceordose‐

effectrelationship.Thisusuallyinvolvesestimatinghistoricalexposuresforasegmentof

thepopulationbasedongeographyorthepopulationatriskforwhichscenario

developmenthassuccessfullybeenused[132].Scenarioevaluationisanindirectestimate

ofexposure.Thismethodtypicallyusesmeasuredconcentrationdatainabulkmediaor

locationincombinationwithamodellingproceduretoeitherextrapolateorinterpolate

exposureconcentrationstoothermediaorlocationsforwhichmeasurementsdonotexist

[131].Environmentalfate[133,134],land‐useregression(LUR)[135,136],anddispersion

models[137]arecommonexamplesofscenarioevaluationmodels.Focusherewillbeon

theapplicationofLURmodelswithrespecttoenvironmentalepidemiologicalresearch.

Anexposuremodelisaconceptualormathematicalrepresentationoftheexposure

processbycombiningrealmeasurementsandapplyingvariousassumptionstoprovide

estimatesofexposure/doseforadefinedpopulation[128].LURmodelsareatypeof

empiricalmodelthatestimatesthestatisticalrelationship(e.g.regression)between

pollutantmeasurementsandland‐usecharacteristicsasindependentvariablestopredict

concentrationsandapplythemtounknownlocationsusinggeographicinformationsystems

(GIS).LURmodelshavesuccessfullybeenusedtopredictwithin‐cityairpollutant

concentrationsusingpredictorvariablessuchastrafficdensity,roadlength,proximityto

majorroadorothersources(e.g.gasstations,airports)[136,138–140].AnationalLUR

modelwasdevelopedbyHystadetal.(2011)thatcapturedbothbetweenandwithin‐city

pollutionvariabilityacrossCanada.PM2.5,NO2andVOCairpollutantswereappliedtostreet

block‐facepointstodeterminepopulationexposureestimates.[141].

WhiletheapplicationofLURsinenvironmentalairpollutionepidemiologicstudies

hasmanyadvantagesoverothermethods[132,135],theydohavecertainlimitations.

AlthoughnotlimitedtoLURmodels,thepresenceofmeasurementerrorandexposure

misclassificationisofupmostimportance.Distinctionbetweentwocomponentsof

measurementerrorshouldbemade.Berkson‐likeerrorcanbeconsideredaspartofthe

trueexposurenotpredictedfromthemodelandwillinflatethestandarderrorsofthe

healtheffectbutinducesverylittletonobiasinthemagnitudeoftheeffectestimate.

Classical‐likeerrorontheotherhandresultsfromuncertaintyinthemeasurementofthe

76

exposuremodelparametersandcanbothinflatethestandarderrorsaswellasbiasthe

effectestimate[142,143].

Themagnitudeoftheerrorinhealtheffectestimatesdependspartlyonthestudy

designaswellasthepollutantbeingmodeled.Netheryetal(2008)conductedaseriesof

studiesintheVancouverBCregionthatevaluatedtheabilityofLURmodelstopredict

personalexposuretoairpollutantsinacohortofpregnantwomen.Sheconcludedthatthe

useofoutdoorconcentrationestimatesweregoodproxiesforexposure[144],andthat

specificallyforPM2.5,incorporatingmobilitypatternsintothemodelsdidnotmarkedly

improvetheexposureassessmentsbasedonpersonalmonitoring[145].Thiswas

attributabletothefactthatPM2.5hasarelativelyhomogenousdistributionwithin

communitiescomparedtoanairpollutantsuchasNO2whichhasmuchgreaterlocalspatial

variability[145,146].ThiswasalsoobservedfortheLURmodelbyHystadetal.[141].

Understandingtheimpactofenvironmentalstressorsonhumanhealthisacomplex

undertaking.Inlightofthesechallenges,explicitattentiontoarelevanttheoretical

frameworkiscriticaltounderstanding,assessing,andcommunicatingcumulativerisk

burdens.Thechallengethenbecomeshowtodevelopthenecessarymodelsandevaluation

methodstoclarifytheetiologicpathwaysandmechanismsthatleadtoadversehealth

outcomesthatoftendisproportionatelyaffectaspecificsub‐population.

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Chapter3:Heavysmokingduringpregnancyasamarkerforotherriskfactorsofadversebirthoutcomes:apopulation‐basedstudyin

BritishColumbia,CanadaEricksonAC,ArbourLT.Heavysmokingduringpregnancyasamarkerforotherriskfactorsofadversebirthoutcomes:apopulation‐basedstudyinBritishColumbia,Canada.BMCPublicHealth.2012;12(1):102.

AbstractBackground:Smokingduringpregnancyisassociatedwithknownadverseperinataland

obstetricaloutcomesaswellaswithsocio‐economic,demographicandotherbehavioural

riskfactorsthatindependentlyinfluenceoutcomes.Usingalargepopulation‐based

perinatalregistry,weassessthequantityofcigarettessmokedforthemagnitudeofadverse

birthoutcomesandalsotheassociationofothersocio‐economicandbehaviouralrisk

factorsdocumentedwithintheregistrythatinfluencepregnancyoutcomes.Ourgoalwasto

determinewhethernumberofcigarettessmokedcouldidentifythoseingreatestneedfor

comprehensiveinterventionprogramstoimproveoutcomes.

Methods:Ourpopulation‐basedretrospectivestudyofsingletonbirthsfrom2001to2006

(N=237,470)utilizeddataobtainedfromtheBCPerinatalDatabaseRegistry.Smokingdata,

self‐reportedattheearliestprenatalvisit,wascategorizedas:never,former,light(1to4),

moderate(5to9),orheavysmoker(10ormoreperday).Crudeandadjustedoddsratios

(AOR)with95percentconfidenceintervals(95%CI)werecalculatedusinglogistic

regressionmodelsforsmokingfrequencyandadversebirthoutcomes.Apartial

proportionalodds(pp‐odds)modelwasusedtodeterminetheassociationbetween

smokingstatusandotherriskfactors.

Results:Therewere233,891singletonbirthswithavailablesmokingstatusdata.A

significantdose‐dependentincreaseinriskwasobservedfortheadversebirthoutcomes

small‐for‐gestationalage,termlowbirthweightandintra‐uterinegrowthrestriction.

Resultsfromthepp‐oddsmodelindicateheavysmokersweremorelikelytohavenot

graduatedhighschool:AOR(95%CI)=3.80(3.41‐4.25);beasingleparent:2.27(2.14‐

2.42);haveindicationofdrugoralcoholuse:7.65(6.99–8.39)and2.20(1.88–2.59)

respectively,attendfewerthan4prenatalcarevisits:1.39(1.23–1.58),andbe

multiparous:1.59(1.51–1.68)comparedtolight,moderateandnon‐smokerscombined.

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Conclusion:Ourdatasuggeststhatself‐reportsofheavysmokingearlyinpregnancycould

beusedasamarkerforlifestyleriskfactorsthatincombinationwithsmokinginfluence

birthoutcomes.Thisinformationmaybeusedforplanningtargetedinterventionprograms

fornotonlysmokingcessation,butpotentiallyothersupportservicessuchasnutritionand

healthypregnancyeducation.

1.0BackgroundSmokingduringpregnancyisassociatedwithknownadverseperinataland

obstetricaloutcomes[1‐4];however,itremainsunclearthemagnitudeastowhichadverse

outcomesarerelatedtocigarettesmokeitselfversussurroundingfactorsdifficultto

quantifyandcontrol.Forinstance,socio‐economicstatus(SES)andpsychosocialstressare

bothassociatedwithadversebirthoutcomes[5‐7]aswellaswithprevalenceofsmoking

duringpregnancy[8‐10].Theseobservationsaresupportedbythemountingbiological

evidenceforastress‐relatedpsychoneuroendocrineprocesscontributingtotheunderlying

etiologyofadversefetaldevelopment[11].Thelinkagesbetweensociallypatternedadverse

healthbehavioursandoutcomesaredifficulttounderstandletaloneseparateandmeasure.

Therefore,itmaybebeneficialtouse(heavy)smokingduringpregnancyasamarkerfor

latentandunquantifiableriskfactorsthatalsoaffectoutcomes.

Inarecentreportassessingthenumberofcigarettessmokedduringpregnancyand

adversebirthoutcomesintheQikiqtaaluk(Baffin)regionofNunavut,‘heavysmokers’

(greaterthantencigarettesperday)hadsignificantlyworsebirthoutcomesthannon‐and

lightsmokers[12].IntheQikiqtaalukpopulationwhere80percentofpregnantmothers

smoke,itwassurprisingtoobservewhatresemblesathresholdeffectofheavysmokingon

adversebirthoutcomes,particularlybirthweight.Adose‐responserelationshipwasalso

observedbetweenlevelofsmokingduringpregnancyandhigherself‐reportingofalcoholor

druguse(predominatelymarijuana).Despitecertainlimitations,theresultsledtothe

conclusionthatheavysmokingmaybeamarkerforadditionalriskfactorsandbeusedto

identifyhighriskpopulationsfortargetedintervention[12].

Inadditiontotheinter‐relationshipbetweenadversebirthoutcomes,SESand

psychosocialstressmentionedabove,heavysmokingcouldalsobemarkerforpoor

nutritionalstatus[13].Smokersingeneralareshowntohavepoorernutritionalprofiles

thannon‐smokersinwhichbehaviouralandbiologicalfactorsindependentlyaccountfor

thedifferences,particularlymicronutrients,essentialmineralsandvitamins[14,15].While

smokerstendedtohavereduceddietaryintakeofsomemicronutrients,theobservedlower

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blood/serumconcentrationswereprimarilyattributedtotheincreasedturnoverof

micronutrientsviaaninflammatoryresponsecausedbytheoxidativestressofsmoking.

Further,incertaincasestheinflammationascribedeffectwasmorepronouncedinlong‐

termandheavysmokers[14].Theeffectsarefurtherconfoundedamongstpregnantwomen

whereithasbeenshownthatheavysmoking,lowsocialclass,rentingaccommodation,and

loweducationpredictpoordietaryintake[16].

Asimportantasunderstandingtheetiologyofadversebirthoutcomes,isidentifying

thoseatparticularriskwhomightbenefitfrominterventionwiththegoalofimproving

outcomesatthepopulationlevel.Thepurposeofthisstudyistwo‐fold:1)toassessthe

quantityofcigarettessmokedandthemagnitudeofadversebirthoutcomes,and2)

determinetheassociationofquantityofcigarettessmokedwithothersocio‐economicand

behaviouralriskfactorsdocumentedwithintheregistrythatalsoinfluencepregnancy

outcomes.Weusedawell‐establishedperinatalregistrydatabasetoaskthequestion:can

highquantitiesofcigaretteusereportedatthefirstprenatalvisitbeusedasasurrogateto

identifyhighriskmothersfortargetedsupportservicesthroughoutpregnancy?

2.0DataandMethodsThispopulation‐basedretrospectivestudyofallsingletonbirths(livebornand

stillbirths)inBritishColumbiafrom2001to2006(N=237,470)utilizedthePerinatal

ServicesBritishColumbia(PSBC)PerinatalDatabaseRegistry,andincludedinformationon

maternal‐infanthealthstatusandoutcomes,reproductivehistory,socio‐demographicsand

residentialpostalcodes.TheBCPDRaccountsfor99%ofabout45,000birthsandstillbirths

peryearoccurringintheprovincefrom20weeksgestationorweighingatleast500grams

atbirth.ThirdpartydataaccessisprovidedbyaPartnershipAccord/Memorandumof

AgreementbetweenallB.C.HealthAuthoritiesandthePSBCthroughtheFreedomof

InformationandPrivacyProtectionAct[17].Researchethicsboardapprovalwasgrantedby

theUniversityofVictoria (ethicsprotocol#:11‐043).

Theoutcomevariablesincludedlowbirthweightatterm(LBW<2,500gwith≥37

weeksgestation),pretermbirth(PTB‐between20and36completedweeksgestation),

intra‐uterinegrowthrestriction(IUGR–identifiedduringtheantenatalperiodusing

ultrasoundimaginggrowthparameters),postnatalsmall‐for‐gestationalagebelowthethird

andtenthpercentilesforweightandsexusingBCspecificbirthcharts(SGA‐3andSGA‐10

respectively)[18],andstillbirths(≥20weeksgestationor≥500g).Out‐of‐province

(n=926),recordsmissinggeographicdataonmaternalareaofresidence(n=129),and

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recordsnotmeetingthecriteriaofarecordedbirthinBC(<20weeksgestationand<500

gramsbirthweight,n=12)wereexcluded.Outcomeswerereviewedforcompletenessand

checkedfordoublecountingbetweenvariables(e.g.stillbirthandSGA).

Smokingdataisusuallycollectedatthefirstprenatalvisitfrom12‐18weeks

gestationandiscategorizedintheRegistryas“never”,“former”,and“current”.Thenumber

ofcigarettessmokedperdaybycurrentsmokerswasavailableasanadditionalcontinuous

variableandwascategorizedintothreelevelsofdailymaternalcigaretteuse:light(1to4),

moderate(5to9),andheavy(10ormore).Intermsofformersmokers,itisunknownwhen

inrelationtothepregnancycessationtookplacepriortothefirstprenatalvisit.Despite

beingnon‐smokers,formersmokerswerenotcombinedwiththe‘never’smokedgroupdue

tosignificantmaternalcharacteristicdifferencesbetweenthem.Theadditionalindividual‐

levelvariablesinclude:maternalage,reproductivehistory(parity≥1),numberofantenatal

carevisits,co‐morbiditiessuchasdiabetes,gestationaldiabetesandhypertensionduring

pregnancy,pre‐pregnancyweight,indicationofdrugoralcoholuse,numberofschoolyears

completedandsingleparentstatus(indicationofsupportduringandafterthepregnancy).

Bivariateoddsratio(OR)testsand95percentconfidenceintervals(95%CI)were

calculatedtoassesstheinfluenceofeachcovariateonbirthoutcomeswiththeresults

informingwhichcovariatestoincludeinthemodels.CrudeandadjustedORswith95%CIs

werecalculatedusinglogisticregressiontoassesstheinfluenceofsmokingrateson

outcomes.Sensitivityanalyseswereconductedtoassesstheinfluenceofattritiondueto

missingdataforsomecovariates.ThisincludedbivariateORteststodeterminethe

likelihoodofadversebirthoutcomesandmaternalcharacteristicsbetweenrecordswith

andwithoutdata.

Inordertodeterminetheassociationbetweenthecovariateriskfactorsandthe

differentlevelsofmaternalsmoking,aspecializedcaseofanorderedlogisticmodelwas

usedcalledthepartialproportionalodds(pp‐odds)model.Anordered(ordinalrank)logistic

modelisequivalenttoaseriesofbinarylogisticregressionswherethedifferentlevelsor

groupranksofthedependentvariablearecombinedandcontrasted[19].Inthiscase,there

arefourordinallevelsofsmokingstatus(Never,Light,MediumandHeavy)where:Level1

iscontrastedwithLevels2,3,and4combined;Levels1and2combinedversusLevels3and

4combined;andLevels1,2,and3combinedversusLevel4.Thepp‐oddsmodelisless

restrictivecomparedtoaregularorderedlogisticmodel(alsoknownasaparallel‐linesor

proportional‐oddsmodel),whichassumesallβregressioncoefficientstobeparallel.The

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pp‐oddsmodeleasesthisrestrictionallowingsomeβcoefficientstobethesameandsome

todiffer[19].Formersmokerswerenotusedinthepp‐oddsanalysisduetoitsnon‐ordinal

status.Interactionsbetweencovariateswerecheckedwithnosignificantinteractioneffects

observed.AllanalyseswereconductedinStata11IC[20].

3.0ResultsBetween2001and2006,therewere236,403singletonbirths≥500gramsorover

20weeksgestationinBC.Amongthem,26,564(11.2%)wereactivesmokers,197,583

(83.6%)reportedneversmoking,and12,256(5.2%)wereformersmokers.Oftheactive

smokers,7,806(3.3%oftotalN)werelightsmokers(1‐4cigarettes/day),5,839(2.5%)

weremoderatesmokers(5‐9cigarettes/day),10,407(4.4%)wereheavysmokers(≥10

cigarettes/day),and2,512(1.1%)hadmissingdataonthenumberofcigarettessmokedper

daywhichwereexcludedfromtheanalysis.Thedistributionofdailycigaretteconsumption

wasexponentialwithnotablespikesatincrementsoffivecigarettesperday(Figure11).A

comparisonofthematernalcharacteristicsacrosssmokinggroupsisprovidedinTable2.

Maternalcharacteristicsvariedsubstantiallyacrosslevelsofmaternalsmoking

(Table2).Motherswhowereheavysmokersweremorelikelytobemultiparous,asingle

parent,hadnotcompletedhighschool,beidentifiedforalcoholordruguseandattended

fewerprenatalcarevisits.Heavysmokerswerelesslikelytohavehadhypertensionduring

thepregnancyandallsmokerswerelesslikelytohavegestationaldiabetes.Thehighest

proportionofsmokerswasunder25yearsofage,buttendedtobelightsmokers(inter‐

quartilerange,IQR=3‐6‐10).Incontrast,thesmallerproportionofoldermothers(≥35)

whodidsmoketendedtobeheavysmokers(IQR=4‐10‐10).Withinagecohorts,athirdof

allmotherslessthan20yearsofageandnearlyaquarterofwomenaged20to24reported

smokingwith11.5and9percentofthosebeingheavysmokersrespectively.Conversely,ten

Figure11:DistributionofMaternalDailyCigaretteConsumptioninBC,2001‐06

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percentoftheoldestthreeagecohortsreportsmokingbuthadroughlytwicethe

proportionofheavysmokersaslightsmokers(3.6and1.6percentrespectively).Consistent

withtheyoungestgroupreportingthegreatestproportionofcurrentsmokers,thetwo

youngestagecohortsalsohadthehighestproportionofformersmokers,13.1and8.5

percentrespectively.

FurtherbivariateORtestswithmaternalagerevealedthatwomenundertheageof

30,butparticularlyteens(under20)andthose20to24weresignificantlymorelikelytobe

identifiedfordruguse,OR(95%CI)=9.06(8.19‐10.03)and4.36(4.00‐4.74)respectively;

alcoholuse,OR(95%CI)=9.44(8.13‐10.97)and3.87(3.40‐4.42)respectively;andattend

fewerthan4prenatalcarevisits,OR(95%CI)=4.45(3.91‐5.07)and2.35(2.12‐2.60)

respectivelycomparedtowomen30to34yearsofage.Lackofhighschoolgraduationfor

womenaged20to24werealsolowcomparedtothe30‐34agecohort,OR(95%CI)=

9.80(8.60‐11.39).Furthermore,amongwomenunder25yearsofage,thosewhowere

heavysmokerswereovertentimesmorelikelybeidentifiedfordrugusethannon‐smokers

whereaslightandmoderatesmokershadabouthalftherisk,OR(95%CI)=10.59(9.46‐

11.86)and6.67(5.79‐7.67)forheavyandmoderatesmokersrespectively.Whileheavy

smokersunder25yearsoldalsohadthehighestriskforalcoholindication,lowprenatal

careattendance,singleparentandnograde12educations,thedifferencesbetweenlevelsof

smokingwerelessstark.

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Table2:MaternalCharacteristicsbySmokingStatusinBC,2001‐2006

BirthsbySmokingStatus,*%Characteristic

Nevern=197,583

Formern=12,256

1–4n=7,806

5–9n=5,839

10+n=10,407

Missingn=2,512

Total(%missing)

Age,yr<20 2.3 7.8 14.0 12.2 9.2 11.8 8,620(3.5)

20‐24 12.4 24.3 32.4 32.7 30.3 29.0 35,817(2.0)

25‐29 27.9 29.5 26.8 27.2 26.8 25.7 65,877(1.0)

30‐34 34.7 24.5 17.4 17.3 20.3 21.1 76,616(0.7)

35‐39 18.7 11.5 7.7 8.8 10.8 10.1 40,808(0.6)

40+ 4.0 2.4 1.7 1.8 2.5 2.2 8,665(0.7)

Parity≥1No 44.3 59.3 54.7 47.3 40.2 52.5 107,381(1.2)

Yes 55.7 40.7 45.3 52.7 59.8 47.5 129,022(0.9)

SingleParentNo 92.7 87.1 76.5 77.8 75.1 75.6 214,054(0.9)

Yes 3.7 8.7 15.0 15.1 16.7 18.3 12,726(3.6)

Unknown 3.6 4.1 8.6 7.1 8.2 6.1 9,623(1.6)

HasGrade12†No 0.8 3.0 5.2 5.2 5.4 3.1 3,301(0.7)

Yes 10.7 12.9 9.6 9.4 6.5 6.7 24,908(2.4)

missing 88.5 84.1 85.2 85.4 88.1 90.2 208,194(1.1)

GestationalNo 93.3 94.6 96.2 95.8 95.1 94.7 221,256(1.1)

diabetesYes 6.7 5.4 3.8 4.2 4.9 5.3 15,147(0.9)

Pre‐existingNo 99.6 99.7 99.6 99.6 99.4 99.6 235,465(1.1)

diabetesYes 0.4 0.3 0.4 0.4 0.6 0.4 938(1.2)

HypertensionNo 97.8 97.1 98.1 98.0 98.2 97.8 228,674(1.1)

inpregnancyYes 2.2 3.0 1.9 2.0 1.8 2.2 5,272(1.0)

AlcoholFlagNo 99.7 98.1 95.9 96.6 95.1 93.8 234,290(1.0)

Yes 0.4 1.9 4.1 3.4 4.9 6.3 2,113(7.4)

DrugFlagNo 99.2 96.9 90.8 90.3 84.6 86.7 231,267(0.9)

Yes 0.8 3.1 9.2 9.7 15.4 13.1 5,136(6.4)

Pre‐pregnancy<55 21.0 16.4 19.0 19.9 18.8 16.4 48,430(0.9)

weight55‐74 42.9 43.6 38.8 38.6 36.2 35.0 99,973(0.9)

>74 14.7 22.0 18.1 18.4 20.3 16.7 36,720(1.1)

missing 21.5 18.1 24.1 23.1 24.7 31.8 51,280(1.6)

PrenatalVisits≥3 92.1 93.4 92.3 91.2 89.5 85.9 217,461(1.0)

<3 1.2 1.2 2.5 3.0 3.4 3.8 3,364(2.9)

missing 6.7 5.5 5.2 5.8 7.1 10.3 15,578(1.7)*Likelihood‐RatioChi‐squaredtestsforindependenceacrossthe5smokingcategorieswereallsignificantatp<0.05,exceptpre‐existingdiabetes(p=0.06).†Maternaleducationwasmeasuredinyearsofeducationwith‘12years’indicatinghighschooleducation.

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Missingvalueswereaconcernintwokeycovariates,educationlevelandpre‐

pregnancyweight,thereforesensitivityanalyseswerecarriedouttodeterminethe

differenceincharacteristicsofthosewithmissingdata.Sensitivityanalysesonthose

missingeducationdata(88.1%)revealedsmallbutsignificantlyincreasedrisksformost

adversebirthoutcomes(ORrangebetween1.11–1.29)andnostatisticaldifferencefor

stillbirths.Light,moderateandformersmokerswerelesslikelytobemissingeducation

datacomparedtothosewhoneversmoked,whiletherewasnodifferencebetweenheavy

smokersandneversmokerswithmissingeducationdata.However,overalldifferencesin

ageandcigarettesconsumptionwerenegligiblewiththeIQRofthosewithandwithoutdata

being3‐6‐10and3‐7‐10respectivelyforcigarettesuseamongsmokersandanidenticalIQR

forage.Thesensitivityanalysesonthoserecordsmissingpre‐pregnancyweightdata

(21.7%)demonstratedsignificantlyincreasedrisksofPTBandstillbirths,asignificantly

lowerriskofIUGR,andnostatisticaldifferencefortheotheroutcomes.Alllevelsofsmoking

weresignificantlymorelikelytobemissingpre‐pregnancyweightdatacomparedto

motherswhoneversmoked,althoughtheIQRofcigaretteusebetweenthosewithand

withoutdataweresimilar(3‐6‐10and3‐7‐10respectively).Intermsofothermaternal

characteristics,thereweresomedifferencesinthevariableswhichweremoreorlesslikely

tobemissingdata,howevernocleartrendswereobserved.

Figure12showstheadjustedoddsratiosofadversebirthoutcomeswithmaternal

smokingstatus(differencesbetweencrudeandadjustedORswereunremarkable).

Comparedtomotherswhoneversmoked,therewasasignificantdose‐dependentincrease

inriskforalladversebirthoutcomeswithmaternalsmokingwiththeexceptionof

stillbirths.Furthermore,heavysmokershadasignificantlygreaterriskofSGA‐3,SGA‐10

andIUGRcomparedtolightandmoderatesmokers.Theadditionoftheeducationvariable

attenuatedtheeffectoflightsmokersforalloutcomesresultinginnosignificantdifference

comparedtonon‐smokers.Similarly,allobservedeffectsofsmokingandPTBwerereduced

tothenullafterincludingtheeducationvariabletothemodel.However,theeducation

variabledidnotaltertheeffectofoutcomesonheavysmokerswhileitstrengthenedthe

effectofmoderatesmokersforSGA‐3,SGA‐10andLBW.

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Resultsfromthepp‐oddsmodeldescribehowdifferentcovariateriskfactors

predicthigherorlowerlevelsofmaternalsmoking(Table3).Allvariablesexceptolder

maternalagewereriskfactorsforsmokingduringpregnancy,butofthose,onlymultiparity

andpre‐pregnancyweightgreaterthan74kilogramspredictedheavysmokingoverthe

lowerlevelsofsmoking.Thisisdemonstratedbytheincreasingeffectofthesetwovariables

acrossthethreecomparisons.Oldermaternalagealsopredictedhigherlevelsofsmoking

despitebeingassociatedwithneversmoking.Youngmaternalage(<25years),single

parent,drugandalcoholindicatorswereallstronglyassociatedwithmaternalsmoking

acrossallcomparisons,butexhibitedtheirstrongesteffectsforLevel‐1(neversmoked

versusallsmokers).Forexample,womenwhosmokedwere10timesmorelikelytobe

indicatedfordrugusecomparedtowomenwhoneversmoked,whileheavysmokerswere

7.6timesmorelikelytobeindicatedfordrugusecomparedtomoderate,lightandnon‐

Figure12:AdjustedOddRatiosofAdverseBirthOutcomesandLevelsofMaternalSmokingSGA‐3–SmallforGestationalAgebelowthe3rdpercentile(n=172,667),SGA‐10–SmallforGestationalAgebelowthe10thpercentile(n=172,667),LBW–LowBirthWeightatterm(n=161,041),PTB–PretermBirth(n=172,690),IUGR–Intra‐UterineGrowthRestriction(n=172,849),Stillbirth(n=173,397).Testswereadjustedfor:maternalage,parity>1,alcoholflag,drugflag,prenatalcarevisits,priorandgestationaldiabetes,hypertensionduringpregnancy,pre‐pregnancyweight,andloneparent.

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smokerscombined.Havingthreeorfewerprenatalcarevisitsandadiagnosisofpre‐

existingdiabetes(p=0.06)mettheparallel‐linesassumption,andthereforehadaconstant

effectacrossalllevelsofcomparison.Thesegeneraltrendsweresustainedwiththeaddition

oftheeducationvariableintothepp‐oddsmodeldemonstratingastrongconstanteffect

acrossalllevelsofcomparison,OR=3.80(95%CI3.41–4.25)withareducedpopulation

sizeof21,775.Onlythevariablesprenatalcareandpre‐existingdiabeteshadtheireffects

significantlyreducedtothenull(p=0.9and0.4respectively).

Table3:OddsRatiosofCovariateRiskFactorsPredictingLevelofMaternalSmokinginB.C.2001–2006(n=163,867)

Characteristic

OR(95%CI)

OR(95%CI)

OR(95%CI)

Level1Vs.Level2+3+4

Level1+2Vs.Level3+4

Level1+2+3Vs.Level4

YoungMaternalAge(<25) 3.66(3.52–3.80) 3.27(3.13–3.42) 2.86(2.70–3.02)

OlderMaternalAge(≥35) 0.67(0.64‐0.71) 0.71(0.67‐0.76) 0.77(0.71‐0.83)

SingleParent 2.42(2.31‐2.53) 2.25(2.14‐2.37) 2.27(2.14‐2.42)

Parity≥1 1.26(1.22‐1.31) 1.49(1.43‐1.55) 1.59(1.51‐1.68)

AlcoholIndication 3.06(2.63‐3.57) 2.41(2.08‐2.81) 2.20(1.88‐2.59)

DrugIndication 10.19(9.32‐11.15) 8.17(7.50–8.90) 7.65(6.99–8.39)

PrenatalCareVisits(≤3) 1.39(1.23–1.58) 1.39(1.23–1.58) 1.39(1.23–1.58)

Pre‐existingDiabetes 1.27(0.99‐1.64) 1.27(0.99‐1.64) 1.27(0.99‐1.64)

Pre‐pregnancyweight(≥75kg) 1.48(1.43‐1.54) 1.49(1.43‐1.56) 1.56(1.48‐1.65)

Level1=neversmoked,Level2=lightsmoker,Level3=moderatesmoker,Level4=heavysmoker.

4.0Discussion Theresultsofthislargepopulation‐basedstudysupportthatsmokingduring

pregnancyisamodifiabledose‐dependentriskfactorofadversefetalgrowththatalsohasa

strongrelationshipwithotherriskbehaviourandlowSESindicators.Comparedtoalllower

levelsofsmoking,heavysmokers(≥10cigarettes/day)hadsubstantiallyworsebirth

outcomesandwerealsoatincreasedrisktobeidentifiedforalcoholuseanddruguse,bea

singleparent,attendedfewerprenatalcarevisitsandhavepre‐pregnancyweightgreater

than74kilograms.AlthoughtheadditionofamajorSESvariable,levelofeducation,was

limitedtoonly10%ofourstudypopulation,themaineffectsandgeneraltrendswere

corroborated.Heavysmokerswere3.8timesmorelikelytohavenotgraduatedhighschool

comparedtomoderate,lightandnon‐smokerscombinedsupportingthepossibilitythat

reportsofsmokinggreaterthantencigarettesperdaymightbeanearlymarkerforthe

needforcomprehensivesupportstoreduceadverseoutcomes.

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TheadjustedORsfortheimpairedfetalgrowthoutcomes(SGA,IUGRandterm‐

LBW)werenearlytwicethemagnitudebetweenheavyandlightsmokers.Theadditionof

theeducationvariableintothelogisticmodelsattenuatedtheeffectoflightsmokerstothe

degreeofnosignificantdifferencebetweenlight,formerandneversmokerswhiletheeffect

ofmoderateandheavysmokingremainedrelativelystablewithroughlydoubletherisk.

TheeffectofsmokingonPTBwascompletelyremovedafteradjustingformaternal

education.ThissuggeststhatwhilebehaviouralandSESindicatorvariables,particularly

maternaleducation,explainsomeoralloftheriskattributedtolightsmoking,heavy

smokingremainsarobustmarkerofincreasedriskfortheimpairedfetalgrowthoutcomes.

Whetherthisobservedeffectisstrictlybiologicalorispartiallyamarkerforsomelatent

unmeasuredriskfactor,heavysmokingreadilyidentifiesapproximatelyfivepercentofthe

BCpopulationwhocouldbenefitfromadditionalsupportservices.Theseresultswere

consistentwithfindingsfromapopulation‐basedstudyfromNovaScotia[21]aswellasa

prospectivecohortstudythatusedanthropometricultrasoundmeasurementstocompare

fetalgrowthinsmokingandnon‐smokingexpectantmothers[22].

Themechanismstowhichcigarettesmokeexposureeffectsfetalgrowthisnot

completelyunderstood;however,IUGRcorrelateswithdefectsinplacentaltransportand

metabolismfunctionswhichseemstorestrictnutrientsupply[23].Zdravkovicetal.report

thatconstituentsincigarettesmokedirectlyaffectplacentalcytotrophoblastproliferation

anddifferentiationwhichreducesbloodflowandcreatesahypoxicenvironment[24].Using

amousemodel,Detmaretal.foundthatpolycyclicaromatichydrocarbons(PAHs),amain

componentincigarettesmoke,causedIUGRinthefetusesofexposeddamsandyielded

alterationsinplacentalvascularisationwithsignificantlyreducedarterialsurfaceareaand

volume[25].PAHsarealsoamainconstituentofvehicularexhaust,particularlydiesel,and

thereismountingevidenceofanassociationbetweensaidpollutantandgrowthrestricted

birthoutcomes[26,27].

Theresultsfromthepp‐oddsmodelshowthatmostofthecovariateriskfactors

primarilypredictmaternalsmokingingeneralversusnon‐smokers.Variablessuchassingle

parent,drugandalcoholindicationandyoungmaternalageweresignificantacrossall

levelscomparison,buthadthestrongesteffectsincomparingnon‐smokerstoallother

levelsofsmoking.Conversely,parityexhibiteditsstrongesteffectsinthethirdcomparison

(heavysmokersversusnever,light,andmoderatesmokerscombined).Thissuggeststhat

whilebeingmultiparousisamarkerformaternalsmokingingeneral,itpredictsheavy

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smokingversusmoderateorlightsmokinghabits.Asimilarobservationwasfoundina

studyofUKwomenregardinggravidaandsmokingbehaviourinsubsequentpregnancies,

commentingonthedoubleexposureofthepreviouschildrentocigarettesmokebothpre‐

andpostnatally[28].Whileoldermaternalagewasassociatedwithhavingreported‘never

smoked’,oldermotherswhodidsmokeweremorelikelytobeheavysmokers.Thistrendof

oldermothersbeingheavysmokerswasalsoobservedintheNunavutchartreviewstudy

[12].

Theresultsforthepp‐oddsmodelincludingmaternaleducationgenerallyholdtrue

tothefirstmodel.Theeffectsforage,parity,singleparent,drugflag,andalcoholflagare

slightlyattenuatedbutremainsignificantwiththesametrend.Education(nograde12)had

astrongconstanteffectonmaternalsmokingacrossalllevelsofcomparison,suggestingan

importantroleinhealthliteracy.Maternallevelofeducationhasbeenshowntobea

powerfuldeterminantofperinatalhealth,independentof,andstrongerthanthatof

neighbourhoodincome[29].Havinglowmaternaleducationalattainment,beingyoungand

asingleparentareindicatorsoflowsocio‐economicstatusthatmayexertadditionalstress

onthepregnancy.Thebiochemicalresponsetostressviaelevatedbasalcortisollevelshas

beenassociatedwithlowbirthweight[30].Threemajorsystemsarethoughttobeinvolved

inthebiologicalpathwaylinkingmaternalmentalhealthandstresswithadversebirth

outcomeswhichincludetheneuroendocrine,theimmune/inflammatory,andthe

cardiovascularsystemswithplacentalcorticotrophinreleasinghormoneplayingacentral

coordinatingrole[31].Indicatorsofwomen’smentalhealthduringpregnancysuchas

psychosocialstress,levelofsocialandfinancialsupportanddepressionmaybeone

possiblepathwaytowhichlowSESisassociatedwithadversebirthoutcomes.

ThemajorityofresultsfromthisBritishColumbiabasedstudywereconsistentwith

recentfindingsfromNorway[32],Germany[33],andanationalCanadiansurveythat

analyzedtheassociatedriskfactorsofsmokingduringpregnancy[8].TheCanadianstudy

foundthatnon‐immigrant,singleparent,lowhouseholdincome,no/littleprenatalclasses,

lesseducation,passive(i.e.partner)smoking,oldermaternalageandahighernumberof

stressfuleventsweresignificantlyassociatedwithmaternalsmokingingeneralbutdidnot

assessquantityofcigarettessmoked[8].AnAustralianstudyofsimilardesigntoour

researchalsousedregistrydataandfoundyoungmaternalage,lackofantenatalcareand

lowSESwereassociatedwithmaternalsmoking[34].Bothpapershighlightedthe

importanceofantenatalcareasacriticalaccesspointtoeducateexpectantmothers

98

regardingahealthypregnancy.Importantly,thestudyfromAustraliafoundthatfirst‐time

mothersandthosewhoaccessedprenatalcareearlyintheirpregnancieshadanincreased

likelihoodofsmokingcessation[34].

TheprovinceofBChasarelativelyhealthybirthingpopulationcomparedtotherest

ofCanadaandhasamongstthelowestratesofmaternalsmokingandexposureto2ndhand

smokeinCanada[35].Further,BChashighgrade12completionratesamongpregnant

womenwhichlikelyinfluencetherelativelylowratesofriskbehaviourssuchasmaternal

smoking.BChadlowerratesofpretermbirthandisaroundtheCanadianaverageforrates

ofSGA.Despitethesepositiveoutcomes,theabilitytorecognizethoseatparticularrisk

earlyinpregnancyandprovidepreventativeprogramscouldhelpachievebetteroutcomes

forallexpectantmothers.Specifically,ourfindingssuggestthatheavymaternalsmoking

willidentifyapproximatelyfivepercentofwomeninBCatparticularincreasedriskof

adverseoutcomesthatmaybenefitfromadditionalservicestopromoteahealthy

pregnancy.Withrespecttoepidemiologicalanalysisofpopulation‐basedperinataldatasets,

thereispotentialtouseheavymaternalsmokingasaproxyforunreliableorunmeasured

individual‐levelbehaviouraland/orsocio‐economicdata.Maternalself‐reportedsmoking

tendstoberoutinelycollectedformostbirthregistriesmakingitanaccessiblevariable

comparedtomanyotherriskfactorvariablesorwhenlinkagetoexternaldatainnot

available.

Therewereseverallimitationstothisanalysis.First,therewerenodataonpassive

smokingrates(i.e.exposuretoenvironmentaltobaccosmokeorhavingapartnerwho

smokes),psychosocialstress,ethnicity,whetherthepregnancywasplanned,birthintervals

formultiparouswomen,potentialoccupationalexposures,orhouseholdincome.Further,

someofthecovariatesexaminedhadhighmissingdata.Asdescribedearlier,pre‐pregnancy

weightwasmissingapproximately25percentofitsvalues,andmaternaleducationdata

wereonlyavailablefor28,210records(12%).Thegreatestconcernwhenanimportant

variableispoorlypopulated,isthattheabsence/presenceofvaluesisbiased(i.e.arenot

missingatrandom).Forinstance,careprovidersmayonlybeaskingthoseindividualsabout

theireducationstatuswhereliteracyisaconcern,andasaresultthedistributionwouldbe

biasedandshiftedtotheleft.Thereforemissingdatanotonlyreducesthestatisticalpower

duetolist‐wisedeletion(i.e.recordswithmissingdataarenotusedinthatparticulartest),

butalsoreducestheoverallreliabilityofthatvariableandpotentiallytheappropriateness

ofthemodel.

99

Toaddressthispotentialbias,sensitivityanalyseswerecarriedoutforlevelof

educationandpre‐pregnancyweight.Thesetestsdemonstratedthatrecordswithmissing

educationdatatendedtohavesmallincreasedrisksforalladversebirthoutcomesand

somematernalriskcharacteristics;whilethosemissingpre‐pregnancyweightdatahad

mixedbirthoutcomesresultsbutincreasedriskformostmaternalcharacteristics.

However,theoverallagestructureandcigaretteconsumptionbetweenthosewithand

withoutmissingdatawerenearlyidenticalwithsimilarmediansandinter‐quartileranges.

Takenasawhole,theseresultssuggestthatthemissingeducationandpre‐weightdatamay

resultinunderestimatingtheriskofsomeadversebirthoutcomesbutthedegreeofmissing

dataisrelativelyconsistentacrossthelevelsofsmokingandthereforeitwouldbepredicted

nottoaffecttheobservedgeneraltrendsofouranalyses.Further,areviewofthemean

numberofyearsofeducationfortheCanadianfemalepopulation(age25‐36)fallwithinthe

meanandstandarddeviationofourmaternalyearsofeducationvariable,14.2versus13.9

±2.6[36].However,giventhestrongassociationbetweenmaternaleducation,smokingand

riskofadverseoutcomes,theseresultsreinforcetheimportanceforthestandardizedand

completecollectionofSESvariablesforallpatientsbyallprenatalhealthcareproviders.

Anotherpotentiallimitationistheself‐reportingbiasofcigaretteconsumption.Self‐

reportedsmokingstatusamongpregnantwomenissusceptibletobias,andmayleadto

attenuationofthetrueeffectofsmokingonbirthoutcomes[37].Ratesofmisclassification

intheUnitedStatesusingdatacollectedfromtheNationalHealthandNutrition

ExaminationSurveys(NHANES)estimatednon‐disclosuretobearound20percent[38].

Therateofmisclassificationwasconsistentwithotherstudies[39],aswasthedemographic

predictorsofnon‐disclosure.Manystudiesusedserum,salivary,orurinarycotinineasa

biomarkertoassessthedegreeofnon‐disclosureinsmokingstatus,andhavefoundarange

ofbetween13‐25percentdependingonthecut‐offvaluesusedtoclassifyoneasanactive

smoker.Non‐disclosurewashigheramongthosewhoreportedtheywereformersmokers,

andyoungermaternalage(20‐24).Thestigmaaroundmaternalsmokingmayleadsome

respondentstounderreporttheiractualconsumptionhabits.Possiblerecallbiascanalso

beassumedgiventhepeaksinthehistogram(Figure11)correspondingatmultiplesof5.

Thiscouldbeduetoresponsesgivenintermsofsomefractioninpacksofcigarettesper

day,suchthathalfapackisequaltotencigarettes.Nonetheless,ourresultssuggestthat

reportednumberofcigarettessmokedcorrelateswithadversebirthoutcomesand

100

associatedsocio‐economicriskfactorssuggestingtheinformationasprovidedwillhelp

identifythoseathighestrisk.

Futureanalysesmayincluderunningahierarchicalmultilevelmodelwiththe

inclusionofneighbourhood‐leveldeprivationscorestodetermineifclusteringof

observationsbyneighbourhoodsregardingbirthoutcomes,smokingratesorprenatalcare

attendance.Thistypeofanalysiswouldbeusefulasabaselinetofurtherstudytheeffectof

localairpollutionexposuremeasuredattheneighbourhood‐levelonbirthoutcomesand

thepotentialinteractionswithSESandotherriskvariables.

5.0ConclusionWehavedemonstratedthatself‐reportsofheavysmoking(≥10cigarettes/day)

earlyinpregnancycouldbeusedasamarkerforlatentorotheroftenunmeasuredlifestyle

riskfactorsthatinfluencebirthoutcomes.Heavysmokershadworseoutcomesandwere

substantiallymorelikelytodemonstrateotherriskfactorscomparedtootherlevelsof

smoking.Whilestrategiesforsmokingcessationareimportantandsupportedbyourstudy,

theunderlyingissuesthatleadtoadversebirthoutcomesmightnotbeaddressedwitha

narrowfocus.Thisinformationmaybeusedforplanningtargetedinterventionprograms

notonlyforsmokingcessationbutpotentiallyothermaternalsupportservicessuchas

nutritionandhealthypregnancyeducationwiththeoverallgoalofoptimizingbirth

outcomes.

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27. PereraFP,RauhV,TsaiWY,KinneyP,CamannD,BarrD,BernertT,GarfinkelR,TuYH,DiazDetal:Effectsoftransplacentalexposuretoenvironmentalpollutantsonbirthoutcomesinamultiethnicpopulation.EnvironmentalHealthPerspectives2003,111(2):201‐205.

28. MorrisM,MaconochieN,DoyleP:Doesgravidityinfluencesmokingbehaviourinpregnancy?Acomparisonofmultigravidandprimigravidwomen.PaediatricandPerinatalEpidemiology2007,21(3):201‐209.

29. LuoZC,WilkinsR,KramerMS:Effectofneighbourhoodincomeandmaternaleducationonbirthoutcomes:apopulation‐basedstudy.CanadianMedicalAssociationJournal2006,174(10):1415‐1420.

30. WustS,EntringerS,FederenkoIS,SchlotzW,HellhammerDH:Birthweightisassociatedwithsalivarycortisolresponsestopsychosocialstressinadultlife.Psychoneuroendocrinology2005,30(6):591‐598.

31. FederenkoIS,WadhwaPD:Women'smentalhealthduringpregnancyinfluencesfetalandinfantdevelopmentalandhealthoutcomes.CNSSpectrums2004,9(3):198‐206.

32. KvalvikLG,SkjaervenR,HaugK:Smokingduringpregnancyfrom1999to2004:astudyfromtheMedicalBirthRegistryofNorway.ActaObstetriciaetGynecologicaScandinavica2008,87(3):280‐285.

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34. MohsinM,BaumanAE:Socio‐demographicfactorsassociatedwithsmokingandsmokingcessationamong426,344pregnantwomeninNewSouthWales,Australia.BMCPublicHealth2005,5:138.

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39. ShiptonD,TappinDM,VadivelooT,CrossleyJA,AitkenDA,ChalmersJ:Reliabilityofselfreportedsmokingstatusbypregnantwomenforestimatingsmokingprevalence:aretrospective,crosssectionalstudy.BMJ2009,339:b4347.

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Chapter4:Thereductionofbirthweightbyfineparticulatematteranditsmodificationbymaternalandneighbourhood‐levelfactors:amultilevelanalysisinBritishColumbia,Canada

EricksonA.C.,OstryA,ChanL.H.M.,ArbourL.Thereductionofbirthweightbyfineparticulatematteranditsmodificationbymaternalandneighbourhood‐levelfactors:amultilevelanalysisinBritishColumbia,Canada.EnvironmentalHealth;2016Apr14;15(1).

AbstractBackground:Thepurposeofthisresearchwastodeterminetherelationshipbetween

modeledparticulatematter(PM2.5)exposureandbirthweight,includingthepotential

modificationbymaternalriskfactorsandindicatorsofsocioeconomicstatus.

Methods:Birthrecordsfrom2001to2006(N=231,929)werelinkedtomodeledPM2.5data

fromanationalland‐useregressionmodelalongwithneighbourhood‐levelSESandsocio‐

demographicdatausing6‐digitresidentialpostalcodes.Multilevelrandomcoefficient

modelswereusedtoestimatetheeffectsofPM2.5,SESandotherindividualand

neighbourhood‐levelcovariatesoncontinuousbirthweightandtestinteractions.

Gestationalagewasmodeledwitharandomslopetoassesspotentialneighbourhood‐level

differencesofitseffectonbirthweightandwhetheranybetween‐neighbourhood

variabilitycanbeexplainedbycross‐levelinteractions.

Results:Modelsadjustedforindividualandneighbourhood‐levelcovariatesshoweda

significantnon‐linearnegativeassociationbetweenPM2.5andbirthweightexplaining8.5%

ofthebetweenneighbourhooddifferencesinmeanbirthweight.Asignificantinteraction

betweenSESandPM2.5wasobserved,revealingamorepronouncednegativeeffectofPM2.5

onbirthweightinlowerSESneighbourhoods.Furtherpositiveandnegativemodificationof

thePM2.5effectwasobservedwithmaternalsmoking,maternalage,gestationaldiabetes,

andsuspectedmaternaldrugoralcoholuse.Therandominterceptvarianceindicating

between‐neighbourhoodbirthweightdifferenceswasreduced75%inthefinalmodel,

whiletherandomslopevarianceforbetween‐neighbourhoodgestationalageeffects

remainedvirtuallyunchanged.

Conclusion:Weprovideevidencethatneighbourhood‐levelSESvariablesandPM2.5have

bothindependentandinteractingassociationswithbirthweight,andtogetheraccountfor

49%ofthebetween‐neighbourhooddifferencesinbirthweight.Evidenceofeffect

modificationofPM2.5onbirthweightacrossvariousmaternalandneighbourhood‐level

105

factorssuggeststhatcertainsub‐populationsmaybemoreorlessvulnerabletoeven

relativelylowdosesPM2.5exposure.

1.0BackgroundStudiesofexposuretoparticulateairpollutionhaveconsistentlyshownan

associationwithlowbirthweight,apredictoroffetalgrowthrestrictionandimportant

determinantofinfantandchildwellbeing[1–3].Thefinefractionofparticulatematter

(PM2.5‐lessthan2.5microns)isacomplexmixtureofelementalandorganiccarbon

compounds,metalsandgasesthatstempredominantlyfromvehicleexhaust,residential

heatingandindustrialemissions.PM2.5,whichincludesultrafineparticleslessthan0.1

microns,canpenetratedeepintothepulmonaryalveolartissuewhereinflammatory

mediatorsandpossiblytheparticlesthemselvestranslocateintothebloodstreamcausing

systemiccardiovascularandimmunologicalalterationssuchasplateletactivation,

coagulationandendothelialdysfunction[4–6].Thesephysiologicalchangesextendtothe

placenta,ahighlyvascularizedorganandextensionofthematernalcardiovascularsystem

withsimilarlyaffectedendothelialcellulartissuesparticularlysusceptibletooxidativeand

inflammatoryinjury[7–9].Excessoruncontrolledoxidativestressandinflammationearly

inpregnancymaydisruptplacentalcellgrowthanddifferentiationpotentiallyleadingto

deficientdeepplacentationandmorphologicaladaptationsassociatedwithseveraladverse

pregnancyoutcomesincludingfetalgrowthrestriction[10].

ThesemechanismsbywhichPM2.5mayacttoadverselyimpactthereproductive

systemarenotfullyunderstood;however,evidencesupportsthepotentialforashared

modeofdevelopmentaltoxicitywithseveralotherknownriskfactors[5,11].Thisincludes

factorsthatalsopromoteorareassociatedwithoxidativestressandinflammationsuchas

smoking[12],druguse[13],advancedmaternalage[14]andgestationaldiabetes[15],and

lowsocioeconomicstatus(SES)ingeneral[16].ThecausalpathwaysinwhichSES

contributestoadversepregnancyoutcomescanbeconceptualizedintermsof‘downstream’

ormediatingexposures,stressesandbehavioursactingontheindividualthrough

‘upstream’society‐leveldeterminantssuchaspoverty,pooreducation,incomeinequality

andsocialdiscrimination/marginalizationoverthelifespan[17,18].Thisimpactofthe

socialenvironmentonhealthbehavioursandoutcomescreateshierarchicalstructures

withinwhichindividualsarenestedinneighbourhoodsandcommunitieswiththeirownset

ofattributesthatcanpromoteorantagonizehealthandhealthybehaviours[19,20].

106

Aparticularchallengeinenvironmentalepidemiologyishandlingdataatdiffering

geographicscales.Birthregistriesandvitalstatisticsprovidedataonindividualbirthsand

certainriskfactors,butmaynothavedataonsociallypatternedriskfactors.Alternatively,

reliableSESdatasuchaseducation,incomeandhousingqualityareoftenonlyavailable

fromnationalcensusdatabasesusingarbitraryadministrativespatialunits.Finally,

obtainingindividual‐levelenvironmentalexposuredataisoftennotpossible.Therefore,the

epidemiologistisoftenleftwithamixofindividual‐levelobservationsclusteredwithin

neighbourhoodareaseachwithdistinctattributes.Theuseofmultilevelstatisticalmodels

separatetheindividual‐leveleffectsfromthecontextoftheirsocialandphysical

environmentsandcanthereforequantifythedegreeofclusteringofindividualswithin

neighbourhoodareasandtestwhetherneighbourhoodfactorsthemselveshavedirect

effectsonthehealthoutcomeoractindirectlyviathemodificationofindividual‐level

variables[21,22].

Throughthemechanismsofoxidativestressandinflammationthereisevidencethat

SESmaynotonlyconfoundbutmodifythePM2.5‐birthoutcomerelationship[23–25].

Variousexposuresandexperiencesmayactinanon‐additivemannertoinfluencefetal

development[5].Wepresentamultilevelcross‐sectionalanalysisoftheassociation

betweenbirthweightandPM2.5inBritishColumbia,CanadawherelevelsofPM2.5are

relativelylowbutcanvarysubstantiallybetweendifferentcommunities[26].Weexplore

thepotentialforbetween‐neighbourhoodvariabilityfortheslopeofgestationalageonbirth

weightandwhetherinteractionswithPM2.5,neighbourhood‐levelSESindicators,and/or

individual‐levelriskfactorsareabletoexplainanyneighbourhood‐levelvariability.Wehad

threeresearchquestions:1)doesexposuretoPM2.5andresidenceinlowSES

neighbourhoodsinBChavesignificantindependentnegativeassociationswithbirthweight;

2)doestheeffectofgestationalageonbirthweightdifferbyneighbourhoods;and3)does

PM2.5interactwithneighbourhood‐levelSESand/orindividual‐levelriskfactorstomodify

theirindependenteffectsonbirthweighttohelpexplainanyneighbourhood‐level

differences.

2.0DataandMethodsThiswasapopulation‐basedretrospectivecohortofsingletonbirthsinBritish

Columbiafrom2001to2006(N=237,470).DatafromtheBCPerinatalDataRegistrywere

providedbyPerinatalServicesBritishColumbia(PSBC)whichincludedinformationon

maternal‐infanthealthstatusandoutcomes,reproductivehistory,maternalriskfactors,

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attributesandresidentialpostalcodes.TheRegistryaccountsfor99%ofbirthsand

stillbirthsinBCofatleast20weeksgestationoratleast500gramsbirthweight.Research

dataaccessisprovidedbyaPartnershipAccord/MemorandumofAgreementbetweenall

BCHealthAuthoritiesandPSBCthroughtheFreedomofInformationandPrivacyProtection

Act[27].ResearchethicsboardapprovalwasgrantedbytheUniversityofVictoria(ethics

protocol#:11‐043).

Theoutcomevariablewascontinuousbirthweightofsingletonbirths.Inorderto

avoidpotentialselectionbias,weincludedallbirths(stillbirthandlive)forallgestational

ages(20‐42weeks).Excludedrecordsincludedout‐of‐provinceandinvalidpostalcodes

(n=1,096),non‐viablebirthspriorto20weeksgestationor500grams(n=14),andthelist‐

wisedeletionofbirthsmissingimportantdataincluding:cigarettessmokedperday

(cigarettes/day,n=2,501),PM2.5(n=1,510),gestationalage(n=373)birthweight(n=41).All

continuousvariables,exceptcigarettes/day,werestandardizedandcenteredtoease

interpretationandaidmodelconvergence.

Thespatiallocationofeachbirthrecordwasgeocodedbasedonthelatitude‐

longitudecoordinateofthemother’sresidentialpostalcodeatthetimeofdeliveryusing

GeoRef[28].Birthrecordswererelatedtotheircorrespondingcensusdisseminationarea

(DA)byperformingapoint‐in‐polygonspatialjoinprocedureinArcGIS10.2[29].DAsare

thesmallestgeographicalunitforwhichcensusdataareavailableandrepresent

neighbourhoodblocksrangingbetween200–800people.WhileDAsdonotnecessarily

representexistingneighbourhoodcommunities[30],theycanactasproxiesforageneral

catchmentareaofpersonalhome‐lifeactivities[31,32].Birthrecordswereidentifiedas

beingeitherruralorurbanusingtheStatisticsCanadaMetropolitanInfluenceZone(MIZ)

codeswhicharebasedoncommutingflowsofsmalltownsintolargercitiesand

metropolitanareas[33].

ExposuretoPM2.5wasestimatedusinganationalland‐useregression(LUR)model

developedtoestimatePM2.5atthecensusstreetblock‐facelevel[34].Themodeluseda

numberofpredictorsincludingsatellitemeasures,proximitytomajorroadsandindustryto

accountfor46%ofthevariabilityinmeasuredannualPM2.5concentrations.Unlikenitrogen

dioxide(NO2),PM2.5tendstohaveamorehomogeneousintra‐urbandistributionbetween

personal,indoorandambient[35].TheLURmodelestimatesusedforthisstudyshowed

verylittlevariabilityofPM2.5exposuresbetweenindividualswithinagivenDA.We

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thereforeaggregatedthepoint‐levelestimatesofPM2.5totheirDA‐levelmeanandrelatedit

toindividualbirthrecordsasanarea‐levelvariable.

TheDA‐levelSESanddemographicdatawererepresentedbythreerelatedbut

independentdatasetsallbasedonthe2006StatisticsCanadanationalcensus.Thefirstwas

aCanadianSESindex(SESi)developedbyChanetal[36].Thesecondwasaneducation

variablerepresentingtheproportionofpopulationover15withanypost‐secondary

education,includingcollege,trades,oruniversity.Thethirdwastheproportionof

continentalAsianimmigrantsbyDAasit’sbeenshowninBCandelsewherethathealthy

babiesfromAsianandSouthAsianbackgroundsareconstitutionallysmallercomparedto

theirCaucasiancounterparts[37,38].AsianandSouthAsianethnicitiesarewell‐

representedthroughoutBCbutparticularlyinconcentratedpocketsthroughoutthemajor

urbancenterofMetroVancouverwherelevelsofPM2.5arealsohigh.Thecorrelation

betweenimmigrantdensitywithSESiandPM2.5was‐0.62and0.53respectively(p<0.001),

wethereforecreatedaresidualimmigrantdensityvariableusingasequentialregression

technique[39].Here,immigrantdensitywasregressedagainstSESiandPM2.5withthe

savedresidualsrepresentingtheuncorrelatedandindependentcontributionofimmigrant

densityonbirthweightfreedfromitscollinearitywithSESiandPM2.5.Thissamemethod

wasusedbetweenSESiandeducation(r=0.25)creatingandresidualeducationvariable.

TheeducationandimmigrantdatawereobtainedbyaccesstoABACUSviatheData

LiberationInitiative[40].

InordertoavoiddatalossfromruralDAs,imputationformissingSES,education

andimmigrantdensityvalueswasperformed.Takingadvantageofthenestedhierarchical

structureoftheadministrativecensusandhealthboundaries,themeanvalueforalarger

encompassingcensussubdivision(CSD)orlocalhealtharea(LHA)wasimputedfora

nestedDAwithamissingvalue.Therewere1,441valuesimputedin52DAsforSESi(0.6%

offinalN,0.8%ofDAs),and3,170valuesimputedin108DAsforbotheducationand

immigrantdensity(1.4%offinalN,1.7%ofDAs).Sensitivityanalyseswereperformedusing

onlythenon‐missingdata.

Hierarchical(multilevel)linearregressionmodelswereusedtotestourresearch

questions,therebyaccountingfortheclustering,ornon‐independence,ofindividuals(level‐

1)belongingtoagivenDAneighbourhood(level‐2).Themultilevelmodelallowsthe

interceptandslopetoactasrandomparametershavingbetween‐area(DA)variabilityfrom

anoverall(BC‐wide)meaninterceptandslope.Thatis,eachDAhasitsowninterceptand

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slopeinwhichtheirvariabilityfromtheoverallinterceptandslopecanbeinvestigatedwith

theadditionoflevel‐1andlevel‐2variablesandtheirinteractions[41].Wefolloweda

bottom‐upapproachtomodelbuildingtoquantifytheexplainedproportionalchangein

variance(PCV),themultilevelmodelequivalenttoanR2[22].Westartedwithanempty

(null)randominterceptmodelwithoutanyindependentvariablesinwhichbirthweightis

onlyafunctionofthemother’sresidentialDA.Thepresenceofsignificantrandomintercept

varianceindicatesthereareunexplaineddifferencesbetweenneighbourhoodmeansof

birthweight.Theproportionofthetotalvarianceinbirthweightthatarisesdueto

neighbourhooddifferencescanbequantifiedbycomputingtheintra‐classcorrelation(ICC),

andhenceprovidesthedegreeofclusteringofindividualbirthweightwithin

neighbourhoods[22].

Gestationalagewasaddedtothenullmodelandgivenarandomslope(i.e.themean

within‐DAeffectofgestationalagewasallowedtodifferbetweenDAs).Thepresenceofa

significantrandomslopeindicatesthatitseffectonbirthweightisnotconstant(orequal)

forallDAs.SubsequentmodelsincludedtheindividualandDA‐levelvariablesalongwith

cross‐levelandwithin‐levelinteractionsinordertoassesstheirfixedeffectsonbirthweight

buttoalsodetermineiftheirinclusionaddressedanyunexplainedinterceptorslope

variance.ModelsweretestedusingtheAkaikeInformationCriterion(AIC)toevaluate

modelperformance.AllstatisticalanalyseswereconductedinStata13IC[42].

Finally,whilemultilevelmodelsaddressintra‐areadependencewhilequantifying

inter‐areavariance,theyassumespatialindependenceamongneighbouringareas.However

environmentalandsocialprocessescanextendbeyondarbitraryneighbourhood

boundaries.Additionallyasmentionedabove,censusDAsdonotnecessarilyrepresent

neighbourhooddynamics,services,infrastructure,etc.;andevidenceofspatialclustering

betweenDAsmayindicatethatanalternativeneighbourhoodarealunitshouldbe

considered.Weusedspatialmethodstotestforthisbycheckingthelevel‐1modelresiduals

andlevel‐2predictedrandomparameters(interceptsandslopes)forspatial

autocorrelationusingthelocalMoran’sIstatistic[43].Thepresenceofsignificantresidual

spatialautocorrelationindicatestheexistenceofunobservedspatialprocessescausingDAs

toclusterandisasignofmodelmisspecification.PredictionoftheDA‐levelrandom

interceptandslopeerrorsusedanEmpiricalBayesmethod[44]availableasapost‐

estimationcommandinStata13IC.

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3.0ResultsAfterexclusionstherewere231,929singleton(liveandstillborn)birthslocatedin

6,338neighbourhoodDAs(min.=1,max.=781,avg.=37).Table4summarizesthe

untransformedindividualandneighbourhoodcovariates(non‐centered,non‐standardized).

Table5reportstheadjustedcoefficientsfortheindividualandDA‐levelcovariatefixed

effectsoncontinuousbirthweight(Model1to3).Gestationalagewasmodeledusinga

quadratictermtoaccountfortherapidfetalgrowthinmid‐gestationsanditsslowergrowth

post‐term(>37weeks).Maternalsmoking(cigarettes/day)andPM2.5werealsomodeled

usingaquadraticterms,bothindicatingasubdueddose‐responsewithincreasingexposure.

Model2addedtheDA‐levelvariablesofSESi,education,immigrantdensityandrural

residence.TheirfixedeffectsshowthatlowerSESandhigherAsianimmigrantdensitywere

significantlyassociatedwithlowerbirthweights(Table5).RuralDAsandDAswithhigher

proportionofpost‐secondaryeducationwerenotsignificantlyassociatedwithbirthweight

inthismodel.However,bothbecamesignificantaftertheadditionofPM2.5andseasonof

birth(coldvs.warm)inModel3.Highereducationhadapositiveassociationwithbirth

weight,whileruralareashadasignificantnegativeassociationwithbirthweight.PM2.5was

foundtohaveasignificantnon‐linearnegativeassociationonbirthweightwherebythe

negativeeffecttapersoffathigherconcentrationsofPM2.5(Figure13).Beingborninacold

(fall‐winter)monthalsohadasignificantnegativeassociationwithbirthweight(Table5).

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Table4:Descriptivestatistics#forindividual(Level‐1)andDA(Level‐2)covariates

Variables Mean Std.Dev. Min‐Max PM2.5Mean(SE)ªLevel‐1(individual) Absence/

1stquintilePresence/

5thquintileBirthweight(grams) 3433.3 566.51 135‐6475 7.30(.016) 7.36(.018)†1

Gestationalage(weeks) 38.8 2.02 19‐44 7.30(.016) 7.30(.017)2

Maternalage(years) 29.8 5.60 11‐55 7.10(.022) 7.39(.014)†

Nulliparous 0.45 0.50 0‐1 7.27(.016) 7.34(.016)†

Gestationaldiabetes 0.06 0.25 0‐1 7.29(.016) 7.53(.016)†

Pre‐existingdiabetes 0.004 0.06 0‐1 7.30(.016) 7.34(.034)

Gestationalhypertension 0.02 0.15 0‐1 7.30(.016) 7.44(.019)†

Poorprenatalcare 0.09 0.29 0‐1 7.28(.016) 7.47(.02)†

Drug/Alcoholflag 0.02 0.15 0‐1 7.31(.016) 7.08(.026)†

Cigarettes/day 0.79 2.91 0‐20 7.33(.015) 7.02(.023)†3

Fall/Winterseason 0.48 0.50 0‐1 7.29(.016) 7.31(.016)†

Level‐2(DA)Variables

SESi ‐0.08 0.58 ‐2.22– 1.18 7.82(.027) 6.95(.032)†‡

Highereducation 0.50 0.12 0– 0.95 7.16(.04) 7.53(.022)†‡

Immigrantdensity 0.16 0.19 0– 0.86 6.75(.023) 7.95(.021)†‡

Ruraladdress 0.11 0.32 0– 1 7.39(.014) 6.59(.061)†‡

PM2.5(μg/m3) 7.30 0.86 4.41‐10.23 ‐‐ ‐‐

#valuesshownareunstandardized,non‐centered;PoorPrenatalCare:havinglessthan4prenatalcarevisitsorwasmissing;Drug/AlcoholFlag:suspectedpossibleuseofillicitdrugsoralcoholbyhealthcareprovider;Cigarettes/day:self‐reportednumberofcigarettessmokeddailyat1stprenatalvisit(excludingnon‐smokers:7.7(5.41));Fall/WinterSeason:birthmonth=Sept–Feb;ªrobuststandarderrorsadjustedfor6338DAclusters;†signi icantdifferenceatp<0.05usingWaldtests;‡1stvs.5thquintile;1normalbirthweightvs.lowbirthweight;2termbirthvs.pretermbirth;3non‐smokervs.currentsmoker.

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Table5:AdjustedindividualandDA‐levelfixedeffectsoncontinuousbirthweight

VariablesLevel‐1(individual)

Model‐1β(95%CI)

Model‐2β(95%CI)

Model‐3β(95%CI)

Gestationalage 310.2(307.6–312.7) 308.7(306.1–311.2) 308.5(306.0–311.1)

Gestationalageª ‐11.6(‐12.2–‐11.1) ‐11.9(‐12.4–‐11.3) ‐11.9(‐12.5–‐11.4)

Maternalage ‐6.6(‐8.6–‐4.7) ‐6.0(‐8.0–‐4.0) ‐4.7(‐6.6–‐2.7)

Nulliparous ‐137.2(‐141.0– ‐133.4) ‐135.7(‐139.5– ‐131.9) ‐134.8(‐138.6– ‐131.1)

Gestationaldiabetes 54.5(47.1– 61.8) 60.0(52.7– 67.4) 62.0(54.6– 69.4)

Pre‐existingdiabetes 320.7(292.1– 349.3) 320.6(292.1– 349.1) 321.2(292.6– 349.7)

Gestationalhypertension ‐90.1(‐102.3– ‐77.9) ‐88.9(‐101.0– ‐76.7) ‐87.6(‐99.7– ‐75.4)

Prenatalcarevisits ‐59.0(‐65.3– ‐52.8) ‐55.0(‐61.2– ‐48.7) ‐52.2(‐58.4– ‐45.9)

Drug/Alcoholflag ‐79.1(‐91.2– ‐67.1) ‐79.2(‐91.2– ‐67.2) ‐81.7(‐93.7– ‐69.7)

Cigarettes/day ‐20.8(‐22.5–‐19.0) ‐22.0(‐23.8–‐20.3) ‐22.7(‐24.4–‐20.9)

Cigarettes/dayª 0.63(0.51–0.74) 0.68(0.57–0.79) 0.7(0.59–0.82)

Fall/Winterseason –– –– ‐6.8(‐10.4–‐3.2)

Level‐2(DA)

SESi –– 37.4(35.2–39.7) 29.4(27.0–31.8)

Highereducation –– ‐2.1(‐4.4–0.2) 3.0(0.7–5.3)

Immigrantdensity –– ‐29.2(‐31.4–‐26.9) ‐31.3(‐33.5–‐29.1)

Ruraladdress –– 4.8(‐3.4–12.9) ‐14.6(‐22.6–‐6.7)

PM2.5 –– –– ‐23.9(‐26.5–‐21.3)

PM2.5ª –– –– 2.8(1.3–4.3)

SeeTable4legendforvariabledefinitions;ª Variablesweremodeledasquadratics.

Figure13:AdjustedPredictedEffectsofPM2.5onBirthWeightPredictedeffectsofPM2.5onbirthweightwith95%confidenceintervalsareconditionalonmodelcovariatesincludedinModel4.BlackverticallinesrepresentthefrequencydistributionofPM2.5.

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Model4testedinteractionswithPM2.5includingcross‐level(level‐1bylevel‐2)and

level‐2bylevel‐2interactionstoexplainthebetween‐DArandominterceptvariability.The

modelresultsarepresentedinTable6includingthemaineffectsaswellastheinteraction

effectswithPM2.5.FourmaternalvariablesshowedeffectmodificationwithPM2.5onbirth

weight.Maternalsmokingandsuspecteddrugoralcoholusebothhadpositiveinteractions

withPM2.5onbirthweightrevealingasubduedassociationwithincreasedPM2.5exposure

(Figure14Aand14Brespectively).MaternalagewasalsomodifiedbydifferencesinPM2.5

exposurewithyoungermaternalagesshowingalargerreductioninbirthweightwith

increasedPM2.5exposure(Figure14C).Alternatively,gestationaldiabeteswasassociated

withamuchgreaterreductioninbirthweightwithincreasingPM2.5comparedtonormal

births,essentiallynullifyingthehigherbirthweightsproducedbythecondition(Figure

14D).ThreeDA‐levelvariablesshowedsignificanteffectmodificationwithPM2.5onbirth

weight.First,theinteractionbetweenSESiandPM2.5revealedamorepronouncedeffectof

PM2.5inlowerSESneighbourhoods(Figure15A).HigherAsianimmigrantdensitybuffered

thePM2.5effect(Figure15B),whileruralDAsshowedanadditionalreductioninbirth

weightwithincreasingPM2.5levelscomparedtourbanDAs(Figure15C).

Therandomeffects,theexplainedproportionalchangeinvariance(PVC),andmodel

diagnosticsarepresentedinTable7.TheunadjustedICCfortheNullrandomintercept

modelwas0.019,indicatingthat1.9%ofthetotalresidualdifferencesinbirthweightare

attributabletoDA‐levelcontextualfactors.Withtheinclusionofthelevel‐1covariatesalong

withtherandomslopeforgestationalagetheICCadj,nowconditionalonmean‐centred

gestationalage(i.e.38.8weeks),increasedto2.3%.Thiswasduetothelargereductionin

thelevel‐1residualvariance(560.5to435.2)relativetothereductioninthelevel‐2random

interceptvariance(78.8to67.0).TheadditionofDA‐levelvariablesinModel2andModel3

removedalotoftheDA‐levelvariancereducingtheICCadjto1.1%and0.8%respectively.

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Table6:AdjustedindividualandDA‐levelfixedeffectsoncontinuousandtermbirthweightandtheirmodificationbyPM2.5(Model‐4)Variables MainEffect

β(95%CI)ModificationbyPM2.5

β(95%CI)Corresponding

Figure

PM2.5ª ‐22.4(‐25.2–‐19.7) 4.9(3.2–6.7) 1

Cigarettes/dayª ‐22.0(‐23.8–‐20.2) 2.8(2.2–3.4) 14A

Drug/Alcoholflag ‐80.6(‐93.0–‐68.2) 15.3(4.4–26.2) 14B

Maternalage ‐4.2(‐6.2–‐2.2) 5.5(3.7–7.4) 14C

Gestationaldiabetes 70.2(62.6–77.8) ‐33.8(‐41.9–‐25.8) 14D

SESi 30.2(27.7–32.7) 4.6(2.0–7.2) 15A

Immigrantdensity ‐33.3(‐35.8–‐30.7) 6.3(3.3–9.2) 15B

Ruraladdress ‐29.1(‐39.1–‐19.1) ‐16.4(‐24.5–‐8.3) 15C

TermBirths,excludingstillbirthsandcongenitalanomalies(N=207,405)

PM2.5ª ‐24.1(‐26.9–‐21.2) 5.0(3.2–6.8) ‐‐

Cigarettes/dayª ‐21.9(‐23.8–‐20.0) 2.8(2.2–3.4) ‐‐

Drug/Alcoholflag ‐80.1(‐93.6–‐66.6) 13.2(1.2–25.2) ‐‐

Maternalage ‐3.1(‐5.2–‐1.1) 5.0(3.1–6.9) ‐‐

Gestationaldiabetes 60.2(52.1–68.4) ‐30.7(‐39.2–‐22.1) ‐‐

SESi 30.5(27.9–33.1) 4.3(1.5–7.0) ‐‐

Immigrantdensity ‐36.0(‐38.7–‐33.3) 6.9(3.8–9.9) ‐‐

Ruraladdress ‐31.2(‐41.6–‐20.8) ‐16.9(‐25.3–‐8.5) ‐‐

ªModeledasaquadratic,Cigarettes/day:0.7(0.6–0.8);Modeladjustedforgestationalage,nulliparous,diabetesmellitus,gestationalhypertension,prenatalcarevisits,seasonofbirth,DA‐leveleducation.

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Figure14:AdjustedPredictedEffectsofmaternalriskfactorsonbirthweightacrosslevelsofPM2.5(A)MaternalSmoking(B)SuspectedDrugorAlcoholUse(C)MaternalAge(D)GestationalDiabetes.Predictedeffectsonbirthweightwith95%CIsareconditionalonmodelcovariatesincludedinModel4.BlackverticallinesrepresentthefrequencydistributionofPM2.5.

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Thelevel‐2randominterceptstandarddeviationindicatesthatthemeanbirthweight

foreveryDAhasadegreeofvariabilityfromtheoverall(BC‐wide)meanbirthweight.For

theNullmodel,theoverallbirthweightinterceptis3,434.2gramswithastandarddeviation

of78.8givingan8.6%differenceinrangebetween95%oftheDAs(3,434.2±(1.96x78.8)

=3,280.0and3588.8grams).Similarly,wecalculatethebetween‐DA95%distributional

rangeofslopesforgestationalagetofallbetween255.8and361.6grams(308.7±(1.96x

26.6)),a29.3%differenceinhowoneweekgestationincreasesbirthweightbetweenDAs.

Figure15:AdjustedPredictedEffectsofDA‐levelfactorsonBirthWeightacrosslevelsofPM2.5(A)SocioeconomicStatusIndex(SESi)(B)AsianImmigrantDensity(C)RuralResidence.Predictedeffectsonbirthweightwith95%CIsareconditionalonmodelcovariatesincludedinModel4.BlackverticallinesrepresentthefrequencydistributionofPM2.5.

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Table7:RandomeffectsandmodeldiagnosticsfromhierarchicallinearmodelsforcontinuousbirthweightinBC,CanadaRandomEffects&ModelDiagnostics

NullModel

Null+r.slope

Model‐1(level‐1)

Model‐2(SES)

Model‐3(PM2.5)

Model‐4(PM2.5interact)

VarianceComponents L1residual(sd) 560.5 442.9 435.2 435.3 435.3 435.1L2intercept(sd) 78.8 67.6 67.0 45.9 39.7 37.9L2slope(sd) ‐‐ 30.1 26.6 27.0 27.2 27.4Intercept 3434.2 3448.4 3524.2 3521.3 3523.4 3522.6AIC 597007 489148 480867 479403 478997 478753L1‐PCV(%) Ref 37.5 39.7 39.7 39.7 39.7L2‐PCV(%) Ref 26.4 27.7 66.0 74.5 76.8ICC/VPC# 0.019 0.023 0.023 0.011 0.008 0.008L1Moran’sI˟ 0.122 0.108 0.105 0.043 0.022 0.018L2riMoran’sI˟ 0.300 0.301 0.310 0.113 0.079 0.071L2rsMoran’sI˟ ‐‐ 0.018 0.018 0.018 0.017 0.017

L1:level‐1=individual‐level;L2:level‐2=DA‐level;sd:standarddeviation;AIC:AkaikeInformationCriterion;PCV:proportionalchangeinvariance;#ICC:Intra‐classcorrelation–iscalledtheVPC(variancepartitioncoefficient)whenconditionalontherandom‐slopevariable,thusvaluesintablerepresentinterceptsforindividualswithmeangestationalage(~39weeks);L2ri:level‐2randomintercept;L2rs:level‐2randomslope;˟allresultsweresignificantp<0.05with999permutationsusingaqueencriterionspatialweightmatrix.

Thelevel‐1andlevel‐2explainedPCV(L1‐PCV&L2‐PCV)summarizestherelative

degreeofexplainedvarianceatthedifferentlevelsbetweenthedifferentmodels.Usingthe

Nullmodelasthereference,theL1‐modelresultedinanL1‐PCV&L2‐PCVof39.7%and

27.7%respectively(Table7).ThesearefairlylargePCVs,indicatingthewithinand

between‐DAvarianceinbirthweightshownintheNullmodelwasmoderatelyattributable

totheseindividual(level‐1)compositionalfactors,largelygestationalage.Theadditionof

theDA‐levelvariablesinModel2explainedanadditional38.3%oftheDA‐levelvariance

(cumulativeL2‐PVC=66%).TheadditionsofPM2.5andseasonofbirthinModel3further

explainedanadditional8.5%ofthelevel‐2interceptvariancebeyondthatofModel2.

Model4accountedforanadditional2.3%L2‐PCV.

Spatialanalyseswereusedasamodeldiagnostictotestforsignificantspatial

autocorrelationofthemodelresiduals.ThelocalMoran’sIstatisticsreportedinTable7

indicatethedegreeoflocalspatialautocorrelation(i.e.spatialclustering)ofthelevel‐1(L1)

residualsaswellasthelevel‐2predictedrandomintercepts(L2ri)andslopes(L2rs)forall

fivemodels.Interpretedinasimilarmannerasaregularcorrelationcoefficient,theMoran’s

Istatisticrevealtheexistenceofsignificantlocalizedclusteringofresidualsatbothlevel‐1

andlevel‐2intheNullandlevel‐1model.Theadditionoflevel‐2variablesreducedthe

Moran’sIsubstantially;howeversmallbutsignificantclusteringremained.

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Sensitivityanalysesusingonlythenon‐imputedDAs(N1=228,765in6,230DAs)

showedveryminordifferencesinmagnitudeofsignificantvariablesinthebirthweight

models.Inasecondsensitivityanalysis,werestrictedthesampletoonlytermbirths

excludingstillbirthsandcongenitalanomalies.Asexpected,therewasalargereductionin

therandom‐slopevarianceforgestationalageduetodroppingpretermbirthsbutnoother

differencesintheobservedrelationships(Table6,bottomhalf).

Finally,acheckforpotentialcolliderbiaswasperformedbyomittinggestationalage

asacovariatefromthemodels[45].Randominterceptmodelsequivalenttothose

presentedinTables5and6wererunandassessedfordifferences.Manyoftheindividual‐

levelcovariatereturnedtoresembletheirunadjustedestimateslistedinTable4.The

interactionbetweenPM2.5andcigarettes/dayremainedunchanged,whereasthePM2.5

interactionwithdrugoralcoholflagwasnolongersignificant(p=0.066).Theinteraction

betweengestationaldiabetesandPM2.5wasreducedbyhalf,butwasstillsignificant.The

effectsoftheDA‐levelvariablesSESiandimmigrantdensityincreasedmoderatelybeyond

their95%CIslistedforModel4.TheeffectforPM2.5decreasedbutwasstillsignificant(‐

18.0(95%CI‐21.3‐‐14.7)).TheDA‐levelinteractionbetweenPM2.5immigrantdensitywas

reducedbyhalfandmarginallynotsignificant(p=0.051),whiletheinteractionbetween

SESiandPM2.5wasalsoreducedtonon‐significance(p=0.33).

AdditionalTablesandFiguresofmodelresultsanddiagnosticsareavailablein

Appendix2.Resultsandinterpretationregardingtheothermeasuresofimpairedbirth

weight(SGA,tLBW,andIUGR)andpretermbirthareavailableinAppendix3.

4.0Discussion Thisstudyemployedmultilevelrandomcoefficientmodelstoassesstheeffectof

PM2.5onbirthweightandtestingitsinteractionwithindividualandneighbourhood‐level

riskfactors.Ourresultsshowthatindividualandneighbourhood‐levelfactorsarecapable

ofmodifyingtheassociationbetweenPM2.5exposureandfetalgrowth.Furthermore,

throughtheuseofrandom‐slopesmodelsweshowthattheeffectofgestationalageonbirth

weightcanvaryconsiderablybetweenneighbourhoodDAswhichwasonlymoderately

addressedinourmodels.Afteradjustingforindividual‐levelcovariatesandDA‐levelsocio‐

economicandsocio‐demographicvariables,wefoundasignificantnon‐lineareffect

betweenPM2.5andbirthweightin231,929birthsinBritishColumbia,Canada.This

associationwasrobusttotheexclusionofstillbirthsandcongenitalanomaliesaswellasthe

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useofonlytermbirthsandmodelsdroppinggestationalageasacovariate,demonstrating

thatselectionbiasdoesnotaffecttheobservedresults.

Ourresultscorroboratethegrowingliteraturesupportinganegativeassociation

betweenPM2.5andbirthweight[3,46,47].Eveninsettingsofrelativelylowairpollution

exposuresimilartoourstudy,significantreductionsinbirthweighthavebeenobserved

[48].Thisstrengthenstheevidenceofthelow‐doseeffectsofPM2.5andisexemplifiedby

Figure13whichshowsthelargestpotentialeffectsonbirthweightareseenatthelowto

midconcentrationsofPM2.5,anotuncommondose‐responsephenomenonalsoobservedin

otherexposure‐diseasecontexts[49].Otherstudiestestingfornon‐lineareffectsoftraffic‐

relatedairpollutantsonfetalgrowthhavebeenmixed[23,50,51].Interestingly,wefounda

similarnon‐lineardose‐responsebetweencigarettes/dayandbirthweight,aneffectalso

shownbyEnglandetalusingbothself‐reportedcigarettes/dayaswellasurine‐cotinine

levelstoassessexposure[52].

OurresultsshowanegativeinteractionbetweenPM2.5andSESsuchthatamore

pronouncedeffectofPM2.5wasseeninlowerSESneighbourhoods(Figure15A).Wealso

observedsignificantinteractionsbetweenPM2.5andAsianimmigrationdensityaswellas

withPM2.5andlivinginarurallocation(Figure15Band15Crespectively).Thissuggests

thatthatnotonlycanneighbourhoodcharacteristicsinfluencefetalgrowthbutcanalso

modifyexposureseitherpositivelyornegatively.Thebiologicalmechanismssupporting

suchinteractionshavebeenrecentlyreviewed[5],andhavebeenindirectlysupportedin

epidemiologicalstudiesthatfoundstrongereffectsofPM2.5acrossrace,ageandSESgroups

[23,25,53].Forexample,theobservedlowerbirthweightsassociatedwithneighbourhoods

withhigherdensitiesofcontinentalAsianimmigrantsislikelyduetoconstitutionalbirth

sizedifferences[37,38],butthepositiveinteractionwithPM2.5mayreflectthebuffering

effectofstrongcommunitycohesivenessandbeneficialculturalpractices[23,32].Asimilar

interactionwasfoundbyBasuetalinwhichbirthstoAsianmothersexhibitedsmallerbirth

weightreductionsforPM2.5constituentscomparedtoCaucasianbirths[23].However,

similarinteractionswerenotfoundbyCurrieetalbetweentraffic‐relatedCOexposureand

riskfactorssuchasrace,education,orlowincome[54].

ThesignificantnegativeinteractionbetweenruraladdressandPM2.5mayreflectthe

underestimationofPM2.5inruralareasbytheLURmodel[34].ThecompositionofPM2.5,

andthusitsrelativetoxicity,isshowntovaryspatiallydependingonitssource(e.g.wood

smokevs.traffic‐relatedemissions)andmaypartiallyexplaintheobservedrural‐urban

120

differences[4,6].Thesignificantnegativeassociationbetweenseasonofbirthandbirth

weightcouldalsoreflecttheincreasedpresenceofwoodheatingandvehicleexhaustin

combinationwithwinterstagnationevents,butcouldalsoreflectachangeindietor

increasedinfectionrates[4,55].AninteractionbetweenseasonofbirthandPM2.5wasnot

statisticallysignificant.

InteractionsbetweenPM2.5andmaternal‐levelvariablesshowntoreducebirth

weightindependentlyrevealedsomecounter‐intuitiveresults.ThisincludedthePM2.5

interactionwithmaternalsmoking(cigarettes/day)andwithsuspecteddrug/alcoholuse

whereincreasingPM2.5levelstemperedthenegativeeffectoftheseriskfactors(Figure14A

and14B).Thisfindingwascounterintuitivetoouroriginalhypothesisandpublished

literature[54],andgaverisetothesuspicionofsurvivalbiasduetocompetingrisks(i.e.

riskbehavioursleadingtoearlymiscarriage,pretermorstillbirths).Althoughwewerenot

abletocontrolforfetallosspriorto20weeksgestation,survivalbiaswasmitigatedby

usinganearfullpopulationsamplethatincludedstillbirths,congenitalanomaliesand

pretermbirths.Furthermore,thepositiveinteractionbetweenmaternalsmokingandPM2.5

wasunchangedafterthesensitivityanalysesusingonlytermbirthsaswellasforpotential

colliderbias;whereastheinteractionbetweendrugandalcoholuseandPM2.5remained

positivebutwasnolongersignificant(p=0.054)[45].Thepersistenceofthisfindingleads

toahypothesisthatsomeindividual‐levelexposuresmayactasapre‐conditioningstress

thatactivatesanadaptiveresponseofincreasedbiologicalresistancetosimilarorother

stressors[56].

AprotectiveeffectofoldermaternalageagainstPM2.5exposurewasalsoobserved

byBasuetal[23],andmaystemfromincreasednutritionalawarenessamongolderwomen

and/ormoresecureincomeandsupportnetworkstherebyreducingpotentialstressand

anxiety[57,58].Currieetalalsofoundsignificantinteractionsbetweentraffic‐related

carbonmonoxideexposureandmaternalage,butthatbothyounger(<age19)andolder(>

age34)maternalagehadgreaterreductionsinbirthweight[54].Gestationaldiabeteshas

beenshowntobeassociatedwithPM2.5andotherairpollutants[59,60];however,their

interactionwithrespecttobirthweighthasnotbeenassessed.Ourstudyshowedthat

pregnanciesaffectedbygestationaldiabeteshadsignificantlyhigherbirthweightsas

expectedbutrevealedasharpreductioninbirthweightwithincreasingPM2.5.This

significantnegativeinteractionbetweenPM2.5andgestationaldiabetescouldberelatedto

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excessofsystemicorplacentaloxidativestressandinflammationresultinginrestricted

fetalgrowth[15,61].

Whiletheapplicationofmultilevelmodelsinperinatalepidemiologyhavebecome

morecommon[62],mosthavebeenrandominterceptmodelswithveryfewincludinga

random‐slopeparameter.Permittingtheslopeforindividual‐levelgestationalagetobe

randomcanelucidatehowitseffectonbirthweightdiffersbetweenDAs.Forexample,the

additionoflevel‐1covariatesreducedtherandom‐slopevariabilityfrom30.1to26.6.This

suggeststhatthatthesematernalriskfactorsactthroughgestationalagetoinfluencebirth

weightandarenotdistributedhomogeneouslyacrossDAs.Inlightofthesefindings,

significantinter‐DAvarianceremainedforboththerandominterceptandslope.Inother

words,despiteexplainingasubstantialproportionofthebetween‐DAvarianceinbirth

weightwithbothlevel‐1compositionalandlevel‐2contextualfactors,thereremained

unmeasuredDA‐levelmechanismsactingeitherdirectlyonfetalgrowthand/orthrough

gestationalagetoproducebetweenneighbourhooddifferencesinbirthweight.

Spatialanalyseswereusedtoexaminethewiderspatialcontextwithinwhichthe

DAsaresituatedaswellasaservingasameasureofmodelspecificationandhowwellthe

chosenrepresentativeneighbourhood(DA)unitperformed.TheinclusionoftheDA‐level

variablesandinteractionssubstantiallyreducedthespatialautocorrelationinthelevel‐1

andlevel‐2randominterceptresiduals(L1&L2riMoran’sIinTable7).Therewasvery

littlespatialautocorrelationinthelevel‐2random‐slopeparameters(L2rsMoran’sI),but

wasalsoslightlyreducedintheDA‐levelmodels.Althoughverysmall,significantspatial

autocorrelationremainedintheresiduals,particularlyfortherandomintercept,which

couldbiasthemodelstandarderrorsandincreasetheriskofmakingTypeIerrors[35].

Akeycomponentofthisresearchwastheuseofaland‐useregression(LUR)model

ofairpollution[34].WhiletheLURmodelwasindependentlyvalidatedandachieveddecent

overallresultsinitspredictedestimates,theverynatureofourstudydesignensuressome

degreeofexposuremisclassificationtoourstudypopulation.Ouranalysiswasbasedon

maternalplaceofresidenceatdelivery,andthereforeintra‐urbancommutingandpotential

inter‐urbanrelocationwithinthepregnancyperiodwasnotaccountedforwhichcould

affecttheresults.Time‐activitypatternsshowthatpregnantwomenspendmoretimeat

homeinthelaterstagesofpregnancy,butmobilitypatternsmaydifferbyage,parityand

SES[63,64].AnotherlimitationregardingthePM2.5exposureassessmentisthattheLUR

modeliscross‐sectionalbasedon2006airqualitymonitoringdata,whilethestudyperiod

122

ofourperinataldatasetspans6years(2001to2006).Wethereforeassumeallpregnancies

wereexposedtothesamelevelsofPM2.5fortheirentirepregnancy,regardlessoftheiryear

ofbirth,basedontheirresidentialDA.Whilethismethodpreventstheassessmentof

exposurewindowsbytrimester,spatiotemporalstudiesofPM2.5haveshownlittletono

differencebetweentrimester‐specificandentirepregnancyeffectsonbirthweight

[3,46,48].Finally,themeanPM2.5concentrationsmaybeunderestimatedbytheLURmodel

withlessvariabilityandmissingseveralhighPM2.5outlierlocationsinBCcomparedto

compiledmonitoreddata[26].Thiscouldpotentiallyresultinanunderestimationofour

observedassociationofreducedbirthweightwithincreasingPM2.5levels.

Wewereunabletocontrolformaternal‐levelSES,andthereforethe

neighbourhood‐leveleffectestimatesandinteractionscouldreflectindividual‐level

differences.Forexample,theprotectiveeffectofoldermaternalagebufferingthePM2.5

effectonbirthweightcouldbeduetoindividual‐levelSESfactorsnotaccountedforinour

modelssuchasdiet,incomeorstress.However,studieshavefoundthatadjustmentfor

individual‐levelmeasuresofSESdidnotsignificantlychangethearea‐levelassociations

[20,65].MaternaleducationisavariableprovidedintheBCPerinatalDataRegistry,butwas

onlyavailablefor10%ofourpopulation.However,theadjustmentforsocially‐patterned

behaviouralriskfactorssuchasmaternalsmoking,suspecteddrugoralcoholuseandlow

numberofprenatalcarevisitswillcontrolforsomeindividual‐levelSESdifferences[66].

5.0ConclusionsThisstudysupportsthegrowingliteratureofaneffectofPM2.5onbirthweightandits

modificationbybothmaternalandneighbourhood‐levelfactors.Mostnotably,itshowsthat

lowerSESneighbourhoodsmaybemorenegativelyaffectedbyhigherlevelsofPM2.5.We

observedbothpositiveandnegativeinteractionsbetweenmaternalfactorsandPM2.5that

requirefurtherscrutinybutmayreflectaPM2.5‐oxidiativestresspathwayexpressedvia

eitherprotectivepre‐conditioningorharmfuloverload.Targetedmunicipal‐level

interventionstoreducePM2.5andimprovedneighbourhoodSESmayhelpimprovebirth

outcomesatthepopulation‐level.

6.0EndnoteforChapter4 Figure16andTable8belowshowtheresultsfromamodelthatwassubsequently

runaftertheinitialmanuscriptforChapter4wasacceptedforpublication.Thismodel

(Model5)doesnotsubstantiallyalteranyoftheresults,interpretation,orconclusionsfrom

thepreviousmodels;however,itdoesprovideamorenuancedunderstandingofthe

123

observedrelationshipsbetweenPM2.5,ruralresidenceandSESoncontinuousbirthweight.

InFigure16,themiddlepane(MeanSES)correspondstoFigure15cinthemaintext

showingthemorepronounceddeclineinbirthweightforruralbirthswithincreasinglevels

ofPM2.5.ByaddinganinteractiontermbetweenruralandSES,thestarkcontrastoftheSES

effectonurbanandruralbirthsbecomesclear.Theredlinerepresentingruralbirths

remainsfairlystatic,indicatingthatwithinruralareasSEShasalimitedroleonbirth

weight,anobservationthathasbeennotedinotherstudies[67,68].Conversely,theblue

linerepresentingurbanbirthsshowsadramaticincreaseinbirthweightwithincreasing

SESwhileitsslopewhichsignifiesthePM2.5effectbecomesslightlylesssteep

demonstratingtheprotectiveroleofimprovedSESonthenegativeeffectsofPM2.5onbirth

weight.ComparedtoModel3and4,Model5showstohaveaslightlybetterAICscore,as

wellasexplainingslightlymoreoftheDA‐levelvarianceintherandomintercept.

Figure16:ResultsfromModel‐5includingaSES*Ruralinteraction

124

Table8:AdjustedindividualandDA‐levelfixedeffectsoncontinuousandtermbirthweightandtheirmodificationbyPM2.5(Model‐5)

Variables MainEffectβ(95%CI)

ModificationbyPM2.5β(95%CI)

PM2.5ª ‐21.0(‐23.7–‐18.2) 4.0(2.2–5.7)Cigarettes/dayª ‐21.9(‐23.6–‐20.1) 2.7(2.1–3.3)

Drug/Alcoholflag ‐80.5(‐92.9–‐68.2) 15.3(4.4–26.2)

Maternalage ‐4.3(‐6.3–‐2.3) 5.5(3.7–7.3)

Gestationaldiabetes 70.3(62.7–77.9) ‐33.7(‐41.8–‐25.7)

SESi 33.7(31.1–36.2) 1.3(‐1.4–3.9)Immigrantdensity ‐30.4(‐33.1–‐27.8) 3.7(0.8–6.7)

Ruraladdress ‐17.7(‐28.3–‐7.1) ‐15.7(‐23.7–‐7.6)

Rural*SESi ‐31.8(‐42.1–‐21.4) ‐‐

RandomEffects&ModelDiagnostics

VarianceComponents L1residual(sd) 435.2

L2intercept(sd) 36.4

L2slope(sd) 27.4

Intercept 3521.22

AIC 478665

L1‐PCV(%) 39.7

L2‐PCV(%) 78.7

ICC/VPC# 0.007

ªModeledasaquadratic,Cigarettes/day:0.7(0.6–0.8);Modeladjustedforgestationalage,nulliparous,diabetesmellitus,gestationalhypertension,prenatalcarevisits,seasonofbirth,DA‐leveleducation,on‐reservebirth.

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Chapter5:Airpollution,neighbourhoodandmaternal‐levelfactorsmodifytheeffectofsmokingonbirthweight:amultilevelanalysis

inBritishColumbia,CanadaEricksonA.C.,OstryA.,ChanL.H.M.,ArbourL.Airpollution,neighbourhoodandmaternal‐levelfactorsmodifytheeffectofsmokingonbirthweight:amultilevelanalysisinBritishColumbia,Canada.BMCPublicHealth.Accepted,inpress.

AbstractBackground:Maternalsmokingduringpregnancynegativelyimpactsfetalgrowth,butthe

effectisnothomogenousacrossthepopulation.Wesoughttodeterminehowthe

relationshipbetweencigaretteuseandfetalgrowthismodifiedbythesocialandphysical

environment.

Methods:BirthrecordswithcovariateswereobtainedfromtheBCPerinatalDatabase

Registry(N=232,291).Maternalsmokingstatuswasself‐reportedasthenumberof

cigarettessmokedperdayusuallyatthefirstprenatalcarevisit.Censusdissemination

areas(DAs)wereusedasneighbourhood‐levelunitsandlinkedtoindividualbirthsusing

residentialpostalcodestoassignexposuretoparticulateairpollution(PM2.5)and

neighbourhood‐levelattributessuchassocioeconomicstatus(SES),proportionofpost‐

secondaryeducation,immigrantdensityandlivinginaruralplace.Randomcoefficient

modelswereusedwithcigarettes/daymodeledwitharandomslopetoestimateits

between‐DAvariabilityandtestcross‐levelinteractionswiththeneighbourhood‐level

variablesoncontinuousbirthweight.

Results:Asignificantnegativeandnon‐linearassociationwasfoundbetweenmaternal

smokingandbirthweight.Therewassignificantbetween‐DAinterceptvariabilityinbirth

weightaswellasbetween‐DAslopevariabilityofmaternalsmokingonbirthweightof

which68%and30%respectivelywasexplainedwiththeinclusionofDA‐levelvariables

andtheircross‐levelinteractions.HighDA‐levelSEShadastrongpositiveassociationwith

birthweightbuttheeffectwasmoderatedwithincreasedcigarettes/day.Conversely,heavy

smokersshowedthelargestincreasesinbirthweightwithrisingneighbourhoodeducation

levels.IncreasedlevelsofPM2.5andimmigrantdensitywerenegativelyassociatedwith

birthweight,butshowedpositiveinteractionswithincreasedlevelsofsmoking.Older

maternalageandsuspecteddrugoralcoholusebothhadnegativeinteractionswith

increasedlevelsofmaternalsmoking.

131

Conclusion:Maternalsmokinghadanegativeandnon‐lineardose‐responseassociation

withbirthweightwhichwashighlyvariablebetweenneighbourhoodsandevidenceof

effectmodificationwithneighbourhood‐levelfactors.Theseresultssuggestthatfocusing

exclusivelyonindividualbehavioursmayhavelimitedsuccessinimprovingoutcomes

withoutaddressingthecontextualinfluencesattheneighbourhood‐level.Furtherstudies

areneededtocorroborateourfindingsandtounderstandhowneighbourhood‐level

attributesinteractwithsmokingtoaffectbirthoutcomes.

1.0BackgroundSmokingduringpregnancyisamodifiableriskfactorassociatedwithadversebirth

outcomesandmayimpartlong‐termhealthconsequences[1–3].Thisrelationshiphowever

isconfoundedbythepresenceofmanyotherriskfactors,includingmaternalage,education,

alcoholordruguse[4–6].Furthermore,it’sbeenshownthattheseindividual‐levelrisk

factorshaveadose‐responseassociationwiththelevelofsmoking,withadistinction

betweenheavysmokers(greaterthan10cigarettesperday)andmoderateorlightsmokers

[4].Forexample,whiletheprevalenceofsmokingduringpregnancydecreaseswith

increasingmaternalage,thelevelofsmokingisheavieramongtheoldermotherswhodo

smoke.Theeffectofsmokingonbirthweighthasbeenshowntobemodifiedbymaternal

ageandotherbehaviouralriskfactors[7,8].Similarly,neighbourhood‐levelfactorsmight

directlyorindirectlymodifytheeffectofsmokingonbirthweightsuchasneighbourhood

deprivationorlevelsofparticulateairpollution[9–11].

Exposuretothefinefractionofparticulatematter(PM2.5,particleswithaerodynamic

diameter≤2.5μm)hasshowntobeaconsistentriskfactorassociatedwithreducedbirth

weight[12].ThecomplexmixtureofPM2.5includeselementalandorganiccarbon

compounds,metalsandgasesthatstempredominantlyfromvehicleexhaust,residential

heatingandindustrialemissions[13].ThemechanismsbywhichPM2.5anditsconstituents

adverselyaffectthereproductivesystemarenotfullyunderstood;however,evidence

supportsthepotentialforasharedmodeofdevelopmentaltoxicitywithtobaccosmoke

exposure[14–16].Withsimilarchemicalcomponents,bothPM2.5andtobaccosmoke

penetratedeepintopulmonaryalveolartissuesandtranslocatetoextrapulmonarytissues

causingsystemiccardiovascularandimmunologicalalterations,includingplatelet

activation,coagulation,endothelialdysfunction,DNAdamageandmutagenesis[13,16,17].

LowSESremainsoneofthemostrobustpredictorsofadversepregnancyoutcomes

suchasfetalgrowthrestrictiondespiteuniversalhealthcareprogramsinCanadaand

132

Europe[9,18,19].Thesociety‐leveldeterminantssuchaspoverty,pooreducation,income

inequalityandsocialdiscriminationandmarginalizationactindirectlyontheplacentaand

fetusthroughthepromotionof‘downstream’ormediatingexposures,stressesand

behaviours[20,21].Instudiesofcardiovasculardisease,neighbourhood‐levelfactorswere

associatedwithincreasedlevelsofsmokingandotherriskfactorssuchasobesity,lackof

exercise,lowerhealthknowledgeandlowerpositivebehaviourchanges[11,22].These

epidemiologicalobservationshavebeenshownwiththeuseofmultilevelstatisticalmodels

capableofseparatingtheindividual‐leveleffectsfromthecontextoftheirsocialand

physicalenvironments[23].Theuseofmultilevelmodelsinperinatalepidemiologyhas

uncoveredneighbourhood‐levelfactorsthatinteractwithmaternal‐levelriskfactorsto

eitherbufferormediateadversebirthoutcomes[21,24].

Wepresentamultilevelcross‐sectionalanalysisofbirthregistrydatainBritish

Columbia,Canada(population4.6million)toinvestigateneighbourhood‐leveldifferencesin

theeffectofcigarettesmokingduringpregnancyonbirthweightandtoquantifythedegree

towhichindividualandneighbourhood‐levelvariablesexplainanyobserveddifferences.

Specifically,wesoughttodeterminewhetherexposuretoPM2.5andlivinginlowSES

neighbourhoodsexplainbetween‐neighbourhooddifferencesintheeffectofmaternal

smokingonbirthweight.Wealsoexaminewhethertheseneighbourhood‐levelfactors

modifythedirecteffectofmaternalsmokingonbirthweight.Birthweightisamongthe

mostimportantfactorsaffectingneonatalmortalityandisasignificantdeterminantofpost‐

neonatalinfantmortalityandchildhoodmorbidity[25].Understandingtheunderlying

individualandinteractiveeffectsofexposuresonbirthweightiscrucialforeffective

communityplanningandstrategicinterventionstoimprovingreproductivehealth

outcomes.

2.0DataandMethodsThiswasapopulation‐basedcross‐sectionalstudyofsingletonbirthsinBritish

Columbiafrom2001to2006(N=237,470).DatafromtheBCPerinatalDatabaseRegistry

wereprovidedbyPerinatalServicesBC(PSBC),andincludedinformationonindividual‐

levelmaternal‐infanthealthstatusandoutcomes,reproductivehistory,socio‐

demographics,riskfactors,andresidentialpostalcodes.TheRegistryaccountsfor99%of

birthsandstillbirthsinBCofatleast20weeksgestationoratleast500gramsbirthweight.

ResearchdataaccessisprovidedbyaPartnershipAccord/MemorandumofAgreement

betweenallBCHealthAuthoritiesandPSBCthroughtheFreedomofInformationandPrivacy

133

ProtectionAct[26].ResearchethicsboardapprovalwasgrantedbytheUniversityof

Victoria(protocol#11‐043).

Theoutcomevariablewascontinuousbirthweightofsingletonbirths.Includedwere

allbirths(stillbirthandlive)forgestationalagesof20to42weeks.Excludedbirthrecords

included:out‐of‐provinceandinvalidpostalcodes(n=1,096),non‐viablebirthspriorto20

weeksgestationandlessthan500grams(n=15),andthelist‐wisedeletionofbirthsmissing

importantdataincluding:cigarettessmokedperday(cigarettes/day,n=2,510),PM2.5

(n=1,512),birthweight(n=46).Table9providesthefulllistofcovariatesusedalongwith

theirsummarystatistics.Allcontinuousindependentvariables,exceptcigarettes/day,were

grand‐meancentredandstandardizedtoeaseinterpretationandaidmodelconvergence.

Thus,avalueofzerorepresentsthetransformedvariable’smeanandreferencevalueand

hasastandarddeviationequaltoone.Thevariablecigarettes/daywaskeptun‐transformed

sincethevaluezero(i.e.non‐smokers)wasthedesiredreferencelevel.Smokinglevelswere

cappedat20cigarettes/daywithhighervaluesassignedavalueof20tostabilizethe

distributiontail(n=245,min.21max.80).Twovariablesindicatingtheuseofalcoholor

druguse(prescription,non‐prescription,illicit)asariskfactorinthepregnancyidentified

byaphysicianwerecombinedintoasingledichotomousvariable.

Birthrecordsweregeocodedbasedonthelatitude‐longitudecoordinateofthe

mother’sresidentialpostalcodeatthetimeofdeliveryusingGeoRefbyDMTI[27].Birth

recordswerethenlinkedtotheircorrespondingcensusdisseminationarea(DA)by

performingapoint‐in‐polygonspatialjoinprocedureinArcGIS10.2[28].DAsrepresentthe

smallestgeographicalunitforwhichcensusdataareavailablewithaspatialcoverage

rangingbetween200–800peopledependingonthelevelofurbandevelopment.WhileDAs

donotnecessarilyrepresentexistingneighbourhoodcommunities[29],theycanactas

proxiesforageneralcatchmentareaofpersonalhome‐lifeactivities[21,30].Birthrecords

wereidentifiedasbeingeitherruralorurbanusingtheStatisticsCanadaMetropolitan

InfluenceZone(MIZ)codeswhicharebasedoncommutingflowsofsmalltownsintolarger

citiesandmetropolitanareas[31].

PM2.5exposurewasestimatedusinganationalland‐useregression(LUR)model

developedtoestimatePM2.5atthecensusstreetblock‐facelevel[32].Themodeluseda

numberofpredictorsincludingsatellitemeasures,proximitytomajorroadsandindustryto

accountfor46%ofthevariabilityinmeasuredannualPM2.5concentrations.Individualbirth

recordswererelatedtotheblock‐facepointestimatesusinganearest‐pointprocedurein

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ArcGIS10.2.Streetblock‐facepointestimateswererelatedtoindividualbirthrecordsusing

anearest‐pointprocedureinArcGIS10.2andthenaggregatedtotheirDA‐levelmeanto

representanarea‐levelairpollutionvariableonindividualbirths.

Threerelatedbutindependentdatasetsallbasedonthe2006StatisticsCanada

nationalcensuswereusedtorepresenttheDA‐levelSESanddemographicdata.Thefirst

wasaCanadianSESindex(SESi)developedbyChanetalwhichprovidesameasureof

overallsocioeconomicneighbourhoodwell‐being[33].Thesecondwastheproportionof

populationover15withanypost‐secondaryeducation,includingcollege,trades,or

universityrepresentinghigherDA‐leveleducationattainmentlevels.Thethirdwasthe

proportionofcontinentalAsianimmigrantsbyDA.It’sbeenshowninBCandelsewhere

thathealthybabiesfromAsianandSouthAsianbackgroundsareconstitutionallysmaller

comparedtoCaucasianbabies[34,35].AsianandSouthAsianethnicitiesarewell‐

representedthroughoutBCbutparticularlyinconcentratedpocketsthroughoutthemajor

urbancenterofMetroVancouverwherelevelsofPM2.5arealsohighandcouldtherefore

confoundanyPM2.5effect.Furthermore,concentratedethniccommunitiesmayimpart

bufferingmechanismsthroughenhancedsocialinteractionsandsupportnetworks[21,24].

AsequentialregressiontechniquewasusedtoremovethecollinearitybetweensetsofDA‐

levelvariables[36].Here,immigrantdensitywasregressedagainstSESiandPM2.5withthe

savedresidualsrepresentingtheuncorrelatedandindependentcontributionofimmigrant

densityonbirthweightfreedfromitscollinearitywithSESiandPM2.5(r=‐0.62and0.53

respectively).ThismethodwasrepeatedforSESiandeducation(r=0.26)creatinga

residualimmigrantdensityandresidualeducationvariable.Theeducationandimmigrant

datawereobtainedbyaccesstoABACUSviatheDataLiberationInitiative[37].

ImputationformissingSES,educationandimmigrantdensityvalueswasperformed

inordertoavoiddatalossofruralDAswithlowpopulationcounts.Takingadvantageofthe

nestedhierarchicalstructureoftheadministrativecensusandhealthboundaries,themean

SESivalueforalargerencompassingcensussubdivision(CSD)orlocalhealtharea(LHA)

wasimputedforanestedDAwithamissingvalue.Therewere1,441valuesimputedin52

DAsforSESi(0.6%offinalN,0.8%ofDAs),and3,170valuesimputedin108DAsforboth

educationandimmigrantdensity(1.4%offinalN,1.7%ofDAs).Sensitivityanalyseswere

performedusingonlythenon‐imputeddata.

Hierarchical(multilevel)linearregressionmodelswereusedtotestourresearch

questions,therebyaccountingfortheclustering,ornon‐independence,ofindividuals(level‐

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1)belongingtoagivenDAneighbourhood(level‐2).Themultilevelmodelallowsthe

interceptandslopetoactasrandomparametershavingbetween‐area(DA)variabilityfrom

anoverall(BC‐wide)meaninterceptandslope.ThereforeeachDAhasitsowninterceptand

slopeinwhichtheirvariabilityfromtheoverallmeaninterceptandslopecanbe

investigatedwiththeadditionofindividual(level‐1)andDA‐level(level‐2)variablesand

theirinteractions[38].Wefollowedabottom‐upapproachtomodelbuildingtoquantifythe

explainedproportionalchangeinvariance(PCV)withtheadditionofsetsofvariables,the

multilevelmodelequivalenttoanR2[23].Westartedwiththeempty(Null)random

interceptmodelwithoutanyindependentvariablesinwhichbirthweightisonlyafunction

ofthemother’sresidentialDA.Thepresenceofsignificantrandominterceptvariance

indicatesthereareunexplaineddifferencesbetweenneighbourhoodmeansofbirthweight.

Theproportionofthetotalvarianceinbirthweightthatarisesduetoneighbourhood

differencescanbequantifiedbycomputingtheintra‐classcorrelation(ICC)which

representsthedegreeofclusteringofindividualbirthweightwithinneighbourhoods[23].

TheNullmodelwasfollowedbyModelthatincludedtheindividual‐levelcovariatesas

wellastheadditionofarandomslopeforthecontinuousvariableofmaternalsmoking

(cigarettes/day,self‐reportedatthefirstprenatalvisit).Byallowingcigarettes/daytobe

random,themeanwithin‐DAeffectofmaternalsmokingisallowedtodifferbetweenDAs.

Thepresenceofasignificantrandomslopeindicatesthatitseffectonbirthweightisnot

constant(orequal)forallDAs;thatis,thereareimportantunexplaineddifferencesbetween

thewithin‐DAgroupeffectsofmaternalsmokingonbirthweight.Subsequentmodels

includedtheDA‐levelvariablesalongwithcross‐levelinteractionstoassesstheirfixed

effectsonbirthweightbuttoalsodetermineiftheirinclusionaddressedanyunexplained

slopevariance.SeveralmodelsweretestedusingtheAkaikeInformationCriterion(AIC)to

evaluatemodelperformance.Wereporttheresultsofthreemodelstocomparethedegree

ofchangebetweenthelevel‐1andlevel‐2homogeneous(non‐interaction)modelsanda

modelwitheffect‐measurevariation.StatisticalanalyseswereconductedinStata13IC[39].

3.0ResultsAfterexclusions,thefinaldatasetincluded232,291singleton(liveandstillborn)

birthslocatedin6,338neighbourhoodDAs(min.=1,max.=782,avg.=37).Table9

summarizestheuntransformedindividualandneighbourhoodcovariates(non‐centered,

non‐standardized).Theprevalenceofmaternalsmokinginthispopulationwas10.3%(n=

23,836)withanaverageof7.5cigarettes/dayamongsmokers.Table10reportsthe

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adjustedcoefficientsfortheindividualandDA‐levelcovariatefixedeffectsoncontinuous

birthweight(Model1and2).Model1wasalevel‐1modelthatincludedonlythematernal‐

levelcovariates.Therelationshipbetweenbirthweightandcigarettes/daywasfoundtobe

non‐linearandwasbestmodeledusingaquadratictermindicatingasubdueddose‐

responsewithincreasingexposure(Figure17).Model2addedtheDA‐levelvariables.Their

fixedeffectsshowthatDAswithhigherSESandhigherproportionofpost‐secondary

educationweresignificantlyassociatedwithhigherbirthweights;whereasDAswith

increasedlevelsofPM2.5,higherAsianimmigrantdensityandruralDAswereall

significantlyassociatedwithlowerbirthweights.Seasonofbirth(fallorwinter)wasalso

significantlyassociationwithreducedbirthweight.TheresultsinTable10representthe

fixedeffectsfromhomogeneousmodels(i.e.thosewithoutanymodeledheterogeneityof

theeffectmeasureformaternalsmoking).

Table9:Descriptivestatistics#forindividual(Level‐1)andDA(Level‐2)covariatesontermbirthweightVariable Mean(sd) Min‐Max

Level‐1(individual)

Maternalage 29.8(5.60) 11–55

Nulliparous 0.45(0.50) 0–1

Drug/Alcoholflag 0.02(0.15) 0–1

Cigarettes/day 0.79(2.91) 0–20

Fall/Winterseason 0.48(0.50) 0–1

Level‐2(DA)Variables

SESi ‐0.08(0.58) ‐2.22–1.18

Education 0.50(0.12) 0–0.95

Immigrantdensity 0.16(0.19) 0–0.86

PM2.5 7.30(0.86) 4.41–10.23Ruraladdress 0.11(0.32) 0–1

#valuesshownareunstandardized,non‐centered;Nulliparous:patienthasneverdeliveredababyofatleast500gramsbirthweightoratleast20weeksgestationinapreviouspregnancy;DrugorAlcoholFlag:physicianindicateduseofdrugs(prescription,non‐prescription,illicit)oralcoholasriskfactorinpregnancy;Cigarettes/day:numberofcigarettessmokeddailyat1stprenatalvisit(self‐reported);Fall/WinterSeason:monthorbirthbetweenSeptembertoFebruary;SESi:socioeconomicstatusindex;Education:proportionofpopulationover15withanypost‐secondaryeducation(trade,college,university);ImmigrantDensity:proportionofthepopulationidentifiedasimmigrantstatusfromcontinentalAsia;PM2.5:ParticulateMatterlessthan2.5microns;Rural:thosehavingaruralresidentialaddress.

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Table10:Adjustedfixedeffectsforlevel‐1andlevel‐2covariatesoncontinuoustermbirthweightVariables Model1

β(95%CI)Model2β(95%CI)

Maternalage ‐16.9(‐19.3–‐14.4) ‐14.9(‐17.4–‐12.4)

Nulliparous ‐107.7(‐112.5–‐103.0) ‐105.5(‐110.3–‐100.7)

Drug/Alcoholflag ‐171.6(‐186.9–‐156.3) ‐172.2(‐187.5–‐157.0)

Cigarettes/daycigarettes/dayª

‐23.5(‐25.8–‐21.2)0.66(0.51–0.80)

‐26.2(‐28.5–‐23.9)0.75(0.61–0.90)

Fall/Winterseason ‐9.6(‐14.1–‐5.0) ‐8.8(‐13.3–‐4.3)

SESi ‐‐ 42.7(39.8–45.6)

Education ‐‐ 6.3(3.5–9.1)

Immigrantdensity ‐‐ ‐35.8(‐38.5–‐33.2)

Ruraladdress ‐‐ ‐18.8(‐28.4–‐9.2)

PM2.5PM2.5ª

‐‐ ‐25.0(‐28.2–‐21.8)3.3(1.5–5.2)

SeeTable9captionforvariabledefinitions;ªModeledasaquadratic.

Model3testedinteractionswithcigarettes/dayincludingcross‐level(level‐1by

level2)andlevel‐1bylevel‐1interactionstoexplainthebetween‐DArandominterceptand

randomslopevariability.ThemodelresultsarepresentedinTable11includingthemain

effectsaswellastheinteractioneffectswithcigarettes/day.Thedegreeofheterogeneity

acrosslevelsofmaternalsmokingmodifiedbytheDA‐levelcontextualfactorsisgraphically

presentedinFigure18.ThefivegraphsshowthepredictedconditionalfixedeffectsofSESi,

education,PM2.5,Asianimmigrantdensityandruralresidenceonbirthweightandtheir

interactionswithspecifiedlevelsofmaternalsmoking(Figure18A,18B,18C,18D,and18E

respectively).Forexample,Table11andFigure18AshowthathigherSEShasasignificant

positiveassociationwithbirthweightbutislesspronouncedwithincreasedlevelsof

maternalsmokingwherebyveryheavysmokers(≥20cigarettes/day)donotincurany

Figure17:AdjustedPredictedEffectsofMaternalSmokingonBirthWeightPredictedeffectsofmaternalsmoking(cigarettes/day)onbirthweightwith95%CIsareconditionalonmodelcovariatesincludedinModel3.Blackverticallinesrepresentthefrequencydistributionofcigarettes/day(non‐smokers,0cigarettes/day,havebeenomittedfordisplaypurposes).

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benefitofhigherSES.Conversely,veryheavysmokersshowedthegreatestgainsinbirth

weightinDAswithhigherproportionsofpost‐secondaryeducatedpeople(Figure18B).

Recallthatthehighereducationvariablewasanuncorrelatedresidualvariableindependent

ofSESi,andthereforetheseobservedassociationsareinadditiontotheeducation‐related

effectcapturedbySESi.

Table11:AdjustedindividualandDA‐levelfixedeffectsoncontinuousbirthweightandtheirmodificationbymaternalsmoking(Model3)

Variables MainEffectβ(95%CI)

ModificationbyCigarettes/dayβ(95%CI)

CorrespondingFigure

Cigarettes/dayª ‐25.7(‐28.1–‐23.3) 0.83(0.68–0.98) 1SESi 43.8(40.9–46.8) ‐2.7(‐3.7–‐1.6) 18AEducation 5.2(2.3–8.1 1.3(0.3–2.3) 18BPM2.5PM2.5ª

‐26.3(‐29.6–‐23.0)3.4(1.5–5.3)

1.8(0.9–2.7) 18C

Immigrantdensity ‐36.5(‐39.2–‐33.7) 2.6(1.5–3.7) 18DRuraladdress ‐15.0(‐25.1–‐5.0) ‐2.9(‐5.6–‐0.2) 18EMaternalage ‐12.1(‐14.8–‐9.5) ‐2.9(‐3.6–‐2.1) 19ADrug/Alcoholflag ‐161.2(‐180.4–‐142.1) ‐3.7(‐6.3–‐1.2) 19BSeeTable9captionforvariabledefinitions;ªModeledasaquadratic;Model3covariatesnotlisted:nulliparousandseasonofbirth.

Figure18:AdjustedPredictedEffectsofMaternalSmokingonBirthWeightacrossDA‐levelFactors.A)SocioeconomicStatusIndexB)ProportionofPopulationwithPost‐secondaryEducationC)PM2.5D)AsianImmigrantDensityE)RuralResidence.Predictedeffectsonbirthweightwith95%CIsareconditionalonmodelcovariatesincludedinModel3.Blackverticallinesrepresentthefrequencydistributionofthevariableonthex‐axis(exceptFigure18Ewhichshowsthefrequencydistributionofcigarettes/day).

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IncreasingPM2.5levelshadasignificantnon‐linearassociationwithreducedbirth

weight;however,itshowedapositiveinteractionwithmaternalsmokingsuchthatthe

effectofincreasedsmokingonbirthweightwasattenuatedinDAswithhigherlevelsof

PM2.5(Figure18C).Similarly,higherAsianimmigrantdensitywassignificantlyassociated

withlowerbirthweightsbuthadapositiveinteractionwithincreasedcigaretteuse

demonstratingaprotectiveeffectofhigherimmigrantdensityDAs(Figure18D).RuralDAs

hadasignificantnegativeinteractionwithmaternalsmokingindicatingafurtherreduction

inbirthweightwithincreasedcigaretteuseamongruralresidents(Figure18E).

Twolevel‐1interactionswithmaternalsmokingweresignificant,maternalageand

suspecteddrugoralcoholuse.Thepredictedconditionalmarginaleffectsofthesetwo

interactionsareshowinFigure19Aand19Brespectivelyindicatingthatthereductionof

birthweightamongheaviersmokersisexasperatedbyoldermaternalageandthose

suspectedofdrugoralcoholuse.Avariableforneighbourhood‐levelsmoking(DA‐average

cigarettes/day)wascreatedandtestedinmodelsalongwithacross‐levelinteractionwith

maternal‐levelcigarettes/daybutneitherparametersweresignificantnorexplainedany

additionalvariability.

Figure19:AdjustedPredictedEffectsofMaternalSmokingonBirthWeightacrossMaternal‐levelFactors.A)MaternalageB)SuspectedDrugorAlcoholUse.Predictedeffectsonbirthweightwith95%CIsareconditionalonmodelcovariatesincludedinModel3.Blackverticallinesrepresentthefrequencydistributionofthevariableonthex‐axis(exceptFigure19Bwhichshowsthefrequencydistributionofcigarettes/day).

Therandomeffects,theexplainedproportionalchangeinvariance(PVC),andmodel

diagnosticsarepresentedinTable12.TheunadjustedICCfortheNullrandomintercept

modelwas0.019,indicatingthat1.9%ofthetotalresidualdifferencesinbirthweightare

attributabletoDA‐levelcontextualfactors.However,theICCincreasedto2.2%forModel1

withtheinclusionofthelevel‐1covariatesandrandomslopeforcigarettes/day.Thiswas

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duetothereductioninthelevel‐1residualvariance(560.5to554.7)relativetotheincrease

inthelevel‐2randominterceptvariance(78.7to83.4).The(nowadjusted)ICCadjis

conditionalfortheindividualcompositionoftheDAs,includingtherandomslopefor

cigarettes/dayheldconstantat0(i.e.non‐smokers).TheadditionofDA‐levelvariablesin

Model2removedalotoftheDA‐levelvariancereducingtheICCadjto0.6%.

Table12:RandomEffectsandModelDiagnostics

NullModel

Model1 Model2 Model3

L1residual(sd) 560.5 554.7 555.0 554.9L2intercept(sd) 78.7 83.4 44.4 44.2

L2slope(sd) ‐‐ 10.7 9.8 9.0

Intercept 3434.3 3505.9 3501.8 3500.9

AIC 602672 598513 596639 596514

L1‐PCV Ref. 2.0% 2.0% 2.0%

L2‐PCV Ref. ‐12.3% 68.2% 68.5%

ICC/VPC# 0.019 0.022 0.006 0.006

Int‐slopecorr. ‐‐ ‐0.53 ‐0.28 ‐0.28

L1residual(sd):Level‐1residualstandarddeviation;L2intercept(sd):Level‐2randominterceptstandarddeviation;L2slope(sd):Level‐2randomslopestandarddeviation;PCV:proportionalchangeinvariance;#VPC(variancepartitioncoefficient)isequivalenttotheICCbutconditionalontherandom‐slopevariable,thusvaluesintablerepresentinterceptsfornon‐smokingindividuals;Int‐slopecorr:intercept‐slopecorrelation.

Thelevel‐2randominterceptvarianceterm(reportedasstandarddeviationsinTable

12)indicatesthatthemeanbirthweightforeveryDAhasadegreeofvariabilityfromthe

overall(BC‐wide)meanbirthweight.FortheNullmodel,theoverallbirthweightintercept

is3,434.3gramswithastandarddeviationof78.7givingan8.6%differenceinrange

between95%oftheDAs(3,434.3±(1.96x78.7)=3,280.0and3588.6grams).The

quadraticformoftherandomslopeforcigarettes/dayinModel1preventsasimilar

calculationtobeperformed,butFigure20givesanindicationofthelargebetween‐DAslope

variabilitywhichshowstheDA‐specificslopesofmaternalsmokingonbirthweight.The

intercept‐slopecorrelationlistedinTable12indicatesthepresenceofDA‐level

heterogeneitysignifyingthatDAswithhigheraveragebirthweightsfromnon‐smoking

mothershavealowerwithin‐DAeffectofsmoking(i.e.higheraverageDAinterceptsofbirth

weighttendtohaveloweraverageslopesforsmoking)[23,38].

Thelevel‐1andlevel‐2explainedPCV(L1‐PCV&L2‐PCV)summarizestherelative

degreeofexplainedvarianceatthedifferentlevelsbetweenthedifferentmodels(Table12).

UsingtheNullmodelasthereference,theModel1resultedinanL1‐PCVof2.0%,andthe

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L2‐PCVintherandominterceptwas‐12.3%.ThenegativeL2‐PVCisaresultofthelarger

level‐2interceptvariancerelativetotheNullmodel.TheadditionoftheDA‐levelvariables

inModel2explained68.2%oftheDA‐levelvariancecomparedtotheNullmodel.Model3

accountedforanadditional0.3%oftheL2‐PCV.

Sensitivityanalysesusingonlythenon‐imputedDAs(N1=229,067in6,230DAs)

showedveryminordifferencesinmagnitudeofsignificantvariables.MostoftheDAsthat

weremissingdatawereinruralareaswithsmallpopulationnumbers,thelikelyreasonwhy

theirdataweresuppressedfromthecensustables.Whilesomeparameterswereslightly

attenuated,manyoftheinteractiontermsincreasedinmagnitude.Therandomintercept

standarddeviationwasalsoslightlysmallerthanthatofthesamemodelusingthefull

datasetwhilerandom‐slopestandarddeviationshowednodifference.Inasecond

sensitivityanalysis,werestrictedthesampletoonlytermbirthsexcludingstillbirthsand

congenitalanomalies.Asexpected,therewasalargereductionintherandomslope

variability(L2slope(sd)=5.7)andasmalldecreaseintherandominterceptvariability(L2

intercept(sd)=43.0)duetousingonlytermbirths.ChangesinthecoefficientsfortheDA‐

levelvariablesaswellastheircross‐levelinteractionswithcigarettes/daywereminorand

withintheir95%confidenceintervalsreportedinTable11.Theexceptionwasthemain

effectofeducationwhichwasnolongersignificant(p=0.151),butitsinteractionwith

cigarettes/dayremainedsignificant(p=0.025).Thematernal‐levelvariableswere

attenuatedbutremainedsignificantwiththeexceptionoftheinteractionbetween

drug/alcoholflagandcigarettes/daywhichwasnolongersignificant(p=0.106).

Figure20:Neighbourhood‐specificslopesofmaternalsmokingonbirthweightEmpiricalBayespredictionsofDA‐specificregressionlinesforModel1.

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4.0DiscussionThisstudyemployedmultilevelrandomcoefficientmodelstoassesswhether

neighbourhood‐levelcontextualfactorscanmodifytheeffectofmaternalsmokingonbirth

weight.Ourresultsshowthattheeffectofmaternalsmokingonbirthweight,self‐reported

asthenumberofcigarettessmokedperday,ismodifiedbybothindividual‐leveland

neighbourhood‐levelvariables.However,theobserveddirectionoftheeffectmodification

wasnotalwaysasexpected.Furthermore,throughtheuseofrandom‐slopemodelswe

showthattheaverageeffectofmaternalsmokingonbirthweightcanvaryconsiderably

betweenneighbourhoodswhichwasonlypartiallyexplainedbythecross‐levelinteractions.

Afteradjustingforindividual‐levelcovariatesandDA‐levelsocio‐economic,socio‐

demographicandairqualityvariables,therewasasignificantnon‐lineareffectbetween

cigarettes/dayandbirthweightinBCforsingletonbirthsfrom2001to2006.This

associationwasrobusttotheexclusionofstillbirthsandcongenitalanomaliesaswellasthe

useofonlytermbirthsdemonstratingthatselectionbiasdoesnotlikelyaffecttheobserved

results.

Theobservednon‐linearassociationbetweencigarettes/dayandbirthweightshown

inFigure17suggeststhatthelargestpotentialeffectsareseenatthelowtomiddlerangeof

smokinglevels.Englandetal[40]foundaverysimilarnon‐linearassociationofmaternal

smokingontermbirthweightusingself‐reportedcigarettes/dayaswellasusingurine

cotinineconcentrations.Therefore,effortstoreducethenumberofcigarettessmoked

duringpregnancymayhavelimitedresultsformoderateandheavysmokerswithout

substantialreductionsorfullcessation[41].Interestingly,wefoundasimilarcurvilinear

relationshipwithincreasinglevelsofmodeledPM2.5andbirthweight(Figure18C),adose‐

responsephenomenonobservedinotherexposure‐diseasecontexts[42].

Beyondthenon‐linearassociationbetweencigarettes/dayandbirthweight,other

factorswereabletomodifythisrelationshipbothpositivelyandnegatively.Ouranalysis

confirmpreviouslyshownmodificationofthesmoking‐birthweightrelationshipby

maternalriskfactors[7,8];however,toourknowledgethisisthefirststudytoshowthat

neighbourhood‐levelfactorsareabletomodifythisrelationship.Wefoundasignificant

negativeinteractionbetweencigarettes/dayandneighbourhood‐levelSESithatresultedin

theattenuationofthebeneficialroleofrisingneighbourhood‐levelSESonbirthweightwith

increasedlevelsofmaternalsmoking.ThepredictedeffectspresentedinFigure18A

suggestsisthatmaternalsmokingmayhavelittlerelevanceinaffectingbirthweightinvery

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lowSESneighbourhoods,butbecomesmoreprominentasneighbourhood‐levelSES

increasesandperhapsotherstressorsnegativelyimpactingbirthweightarereduced.Hence

interventionsfocusingexclusivelyonindividualbehavioursmayhavelimitedsuccess

withoutaddressingthecontextualinfluencesattheneighbourhood‐level[9,43–45].

Conversely,thesmallbutsignificantpositiveinteractionbetweenhigherproportions

ofneighbourhood‐levelpost‐secondaryeducationandcigarettes/dayfoundthatheavy

smokersmaybenefitthemostbylivinginhighereducatedneighbourhoods(Figure18B).

Thistypeofcross‐leveleffecthasbeenobservedinotherepidemiologicalscenarioswhere

higherriskindividualshavebetteroutcomesthanwouldbeexpectedduetosomebeneficial

capacityoftheneighbourhoodcontext[11,22].Themechanismsbywhichneighbourhood‐

levelfactorsaffectindividualhealthisindirectlyexertedthroughindividual‐levelprocesses,

suchasbehaviours,adaptationsandattitudeswhichmaybetransmittedbetweenpeople

[46,47].Mengetalfoundthatloweducationneighbourhoodsexertanimpactonlowbirth

weightandpretermbirththroughunhealthybehaviours,psycho‐socialstress(i.e.senseof

control)andSES‐relatedsupport[21].Thereforeitcouldbethatsmokingcessationratesin

pregnancyarehigherinbettereducatedneighbourhoodswherehealthierbehavioursare

morecommon[48,49].Figure18Bsuggeststhatlivinginhighereducatedneighbourhoods

mayencouragemoderateandheavysmokerstoreducetheirsmokingfrequencytoless

thanfivecigarettes/day.

Neighbourhoodsocialsupportsandtransmissionofbehaviourscouldalsoexplainthe

observedinteractionswithhigherimmigrantdensityandruraladdress,albeitinopposite

directions.Thepositiveinteractionbetweenhigherimmigrantdensityandmaternal

smoking(Figure18D)mayreflectthebufferingeffectofstrongcommunitycohesiveness

andbeneficialculturalpractices[21,43,47].Conversely,theobservednegativeinteraction

betweenruraladdressandcigarettes/day(Figure18E)couldbeduetothetransmissionof

negativebehavioursbeingmorecommon[50],andwherelesssupportforcessationmay

leadtosmokingthroughoutpregnancy[51].Thedichotomizeddefinitionusedtorepresent

ruralresidentialaddressesmayobscuremechanismswhichcanbemodifiedbymaternal

factorssuchaseducation[52].

ThebufferingeffectofPM2.5withincreasedlevelsofmaternalsmoking(Figure18C)is

curiousbutcouldprovideevidenceforaprotectivepre‐conditioningstressthatactivatesan

adaptiveresponseandincreasesbiologicalresistancetocigarette‐inducedharms[53,54].

WefoundasimilarpositiveinteractionbetweensuspectedalcoholanddruguseandPM2.5

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inadifferentanalysis[55].Thesuspicionofsurvivalbiasduetocompetingriskswaspartly

mitigatedbyusinganearfullpopulationsamplethatincludedstillbirths,congenital

anomaliesandpretermbirths,althoughwewerenotabletocontrolforfetallosspriorto20

weeksgestation.Otherexplanationsrequirefurtherscrutinyasevidenceoftheopposite

(negativeandsynergistic)effectbetweensmokingandairpollutantshasbeenshown

[10,16].

Wehaveshowninanearlierpaperthatwomenwhoreportedsmoking10ormore

cigarettes/dayattheirfirstprenatalvisitweresignificantlymorelikelytohaveother

maternalriskfactors,suchaslowereducation,suspecteddrugoralcoholuse,andfewer

prenatalcarevisits[4].Ourcurrentresultscomplimentthispreviousstudybyshowingthat

thecumulativeimpactofmultipleriskfactorscanhavemorethananadditiveeffectonbirth

weightreduction.Thenegativeassociationbetweenoldermaternalageandbirthweight

wasmarkedlygreaterwithincreasedlevelsofmaternalsmoking,particularlyamongthe

heaviestsmokinggroup(Figure19A).Similarly,thosewhoreportedhigherlevelsof

smokingwhowerealsosuspectedofdrugoralcoholuseshowedapronouncedeffect

comparedtothosewhoreportedtonotsmoke(Figure19B).Theseresultscorroboratethe

establishedliteratureshowingsimilarsynergisticinteractionsbetweenbothmaternal

alcoholuseandsmokingonlowerbirthweights[8,56],aswellasbetweenmaternal

smokingandoldermaternalageonbirthweight[7,57].

Whiletheapplicationofmultilevelmodelsinperinatalepidemiologyhavebecome

morecommon[58],mosthavebeenrandominterceptmodelswithveryfewincludinga

random‐slopeparameter.Permittingtheslopeforthematernalcigarettes/dayexposureto

berandomprovidesinformationonhowitseffectonbirthweightdiffersbetween

neighbourhoodsandenablesthesearchforneighbourhood‐levelvariablestohelpexplain

thebetween‐neighbourhoodvariance[38].Forexample,therandom‐slopestandard

deviationpresentedinTable12dropsfrom10.7inModel1to9.0inthefullyadjusted

Model3.Thisrepresentsa30%changeinexplainedrandom‐slopevariance(10.72–

9.02/10.72).Furthermore,theadditionofthelevel‐2variablesexplained68.5%ofthe

randominterceptvariancecomparedtotheNull(empty)model.Howeverinlightofthese

findings,significantinter‐DAvarianceremainedforboththerandominterceptandslope.

Thisstudyusedself‐reportsofsmoking(cigarettes/day)recordedatthefirstprenatal

visit;however,therewerenodataonexposuretoenvironmentaltobaccosmokeorwhether

smokingreductionorcessationoccurredduringthepregnancy.Theself‐reportingbiasof

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cigaretteconsumptioncanleadtotheattenuationofthetrueeffectofsmokingonbirth

weight[59],andmaythereforealterobservedinteractions.Studiesofsmoking

misclassificationintheUnitedStateshaveestimatednon‐disclosuretobearound20%

[60,61].Thedemographicpredictorsofnon‐disclosureincludeformersmokersand

youngermaternalagewhichcouldpartiallyexplaintheobservedinteractionbetween

maternalageandcigarettes/day[61].Similarly,recallbiasandperceivedstigmamayresult

inunder‐reportingofactualconsumptionhabits.Thiscouldaccountfortheobserved

curvilineareffectonbirthweightifwomensmoking10cigarettes/dayreportonlysmoking

5perday,althoughEnglandetalobservedasimilarslopeusingurine‐cotinine

concentrations[40].Whilerelativelysmall,thelist‐wisedeletionofobservationswith

missingsmokingdatamayexcludepotentiallyat‐riskpregnanciesandcouldthereforealter

coefficientestimates(n=2,501,1.1%ofsample).

Anotherlimitationincludespotentialmeasurementerrorandmisclassificationbiasin

thePM2.5exposureassessmentwhichcouldaffectitsestimates.First,theLURPM2.5

concentrationsmaybeunderestimatedwithlessvariabilitycomparedtocompiled

monitoringdatawhichcouldpotentiallyunderestimateitsassociationwithbirthweightin

certainareas[62].AlsothePM2.5LURmodeliscross‐sectionalbasedon2006airquality

monitoringdata,andwethereforeassumethatthestudypopulationwasexposedtothe

samelevelsofPM2.5acrosssixyearstudyperiodbasedontheirresidentialDA.Finally,our

analysiswasbasedonmaternalplaceofresidenceatdelivery,andthereforeintra‐urban

commutingandpotentialinter‐urbanrelocationwithinthepregnancyperiodwasnot

accountedfor.Time‐activitypatternsshowthatpregnantwomenspendmoretimeathome

inthelaterstagesofpregnancy,butmobilitypatternsmaydifferbyage,parityandSES

[63,64].

Amainstrengthofthisstudyisthequalityoftheperinatalregistrydata[65].The

near100%ascertainmentofbirthrecordsfortheprovinceofBCandqualitycontrol

measuresusedindatabasemanagementpracticesproduceshighlyreliabledataon

maternalandnewbornhealthoutcomes,co‐morbiditiesandexposures.However,the

inabilitytocontrolforindividual‐levelSES,particularlymaternaleducation,mayinfluence

theneighbourhood‐leveleffectestimatesandinteractions.Maternaleducationisavariable

providedinthePSBCPerinatalRegistry,butwasonlyavailablefor10%ofourpopulation

cohort.Theadjustmentforsocially‐patternedbehaviouralriskfactorssuchasmaternal

smokingandsuspecteddrugoralcoholusewillcontrolforsomeindividual‐levelSES

146

differences[4].Notwithstanding,ourresultssuggestthatreportednumberofcigarettes

smokedcorrelateswithasubstantialreductioninbirthweightandismodifiedbysocio‐

economic,demographicandenvironmentalriskfactorssuggestingtheinformationas

providedwillhelpidentifythoseathighestrisk.

5.0ConclusionsTheeffectofmaternalsmokingonbirthweightisnotconstantacrossgeography,but

ratheriscontextspecificgiventhesocialandphysicalenvironment.Theuseofrandom

coefficientmodelsrevealedneighbourhood‐leveldifferencesinhowmaternalsmoking

negativelyimpactedbirthweightanddemonstratedeffectmodificationbyneighbourhood

andmaternal‐levelfactors.TheinclusionoftheDA‐levelSES,demographicandPM2.5

variablesexplained68.5%oftherandominterceptvariabilityinDA‐meanbirthweight.

However,therandomslopevariabilitywasonlypartiallyexplainedbythecross‐level

interactionssuggestingothercontextualfactorsareinvolvedindeterminingthemagnitude

ofmaternalsmokingonbirthweight.Furtherstudiesareneededtocorroborateour

findingsandtounderstandhowneighbourhood‐levelattributesinteractwithsmokingto

affectbirthoutcomes.

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Chapter6:AssociationofGestationalDiabetesandHypertensionwithincreasedfineparticulatematterandneighbourhood‐levelsocioeconomicfactors:amultilevelanalysisinBritishColumbia,

CanadaAbstract

Background:Gestationaldiabetes(GDM)andgestationalhypertension(GH)are

independentriskfactorsofpretermbirthandlowbirthweight,andhavebeenassociated

withincreasedexposuretoparticulatematter(PM2.5).Thepurposeofthisresearchwasto

determinetherelationshipbetweenmodeledparticulatematter(PM2.5)exposureand

gestationaldiabetesandhypertensionandthepotentialmodificationbyindicatorsof

socioeconomicstatus(SES)andmaternalBMI.

Methods:Birthrecordsfrom2001to2006(N=166,369and165,727forGHandGDM

respectively)werelinkedtomodeledPM2.5datafromanationalland‐useregressionmodel

alongwithneighbourhood‐levelSESandsocio‐demographicdatausing6‐digitresidential

postalcodes.Logisticmultilevelrandominterceptmodelswereusedtoestimatetheeffects

ofPM2.5,SESandotherindividualandneighbourhood‐levelcovariatesonGDMandGH.

Results:TheincidenceofGHandGDMwas2.4%and6.8%respectivelyacrossthe6‐year

studyperiod.PM2.5wassignificantlyassociatedwithanincreasedriskforbothGHandGDM,

(highestvs.lowestquintile,OR95%CI=1.69(1.51–1.91),and1.63(1.50–1.78),

respectively)afteradjustingforindividualandneighbourhood‐levelcovariates.Higher

neighbourhood‐levelSESaswellashighereducationalattainmentwerebothassociated

withalowerriskofGHandGDM,whilehigherimmigrantdensityandhigherDA‐meanBMI

showedanincreasedrisk.GDMshowedconsiderableeffectheterogeneityinurbanareas

wheretheinteractionbetweenPM2.5andSESgreatlymodifiedtheriskofGDM.These

associationsweremorepronouncedamongmotherswithlargerpre‐pregnancyBMI.

Conclusion:ThisstudysupportsthegrowingliteratureoftheassociationofPM2.5onthe

riskofGHandGDMandprovidesnewinsightsintotheroleofneighbourhood‐levelSESand

demographicfactors.TheimplicationsofapotentiallinkbetweenGHandGDMwithPM2.5

exposureareprofoundasthisairpollutantispervasivethroughouttheworld,andtherefore

evenmodestmitigationinexposurewillprovidesubstantialpublichealthbenefits.

152

1.0BackgroundGestationaldiabetesmellitus(GDM)andgestationalhypertension(GH)aretwo

complexpregnancyconditionsthataffectbetween3‐6%and3‐9%ofpregnancies

respectively,withratesforbothconditionsincreasinginCanada[1–4].GH,definedhereas

excludingthemoreseverediagnosisofpreeclampsia,andGDMarebothassociatedwith

increasedriskofmaternalandfetal/infantmorbiditiesandmortality[3–6].Womenwho

developtheseconditionsduringpregnancyhaveanincreasedlifetimeriskofdeveloping

type2diabetes,cardiovasculardiseaseandobesitylaterinlife[5,7].Althoughtheyare

independentconditions,theyshareseveralriskfactorssuchasincreasedbodymassindex

(BMI),familyhistory,andoldermaternalage[6,8].Recently,thereisaccumulatingevidence

supportingtheirassociationwithexposuretoairpollution[9–15].

Particulateairpollutionlessthan2.5microns(PM2.5)isacomplexmixtureof

elementalandorganiccarboncompounds,metalsandgasesthatstempredominantlyfrom

vehicleexhaust,residentialheatingandindustrialemissions.PM2.5,whichincludesultrafine

particleslessthan0.1microns,canpenetratedeepintothepulmonaryalveolartissue

whereinflammatorymediatorsandpossiblytheparticlesthemselvestranslocateintothe

bloodstreamcausingsystemiccardiovascular,metabolic,andimmunologicalalterations.

Theseincludesystemicinflammation,plateletactivation,coagulation,elevatedleptin,

reducedadiponectin,andendothelialdysfunction[16–22].Increasedoxidativestressand

inflammationinbothGDMandGHhavebeenobservedinpreviousstudies[13,14,16,17].

WhiletheepidemiologicalevidencelinkingPM2.5toacuteandchronichypertensionand

cardiovasculardiseasesiswellestablished[21,23–26],theliteraturesupportingthe

associationbetweendiabetesandPM2.5ismorerecent[16,18,27–29].

Theimpactofthesocialenvironmentonindicatorsandbiomarkersofcardiovascular

andmetabolicsystemdiseasesiswelldocumented[30–34],andlowerSEShasbeen

associatedwithhigherprevalenceofbothGDMandGH[10,35].Thesocialenvironmentcan

beconceptualizedasaseriesofoverlappinghierarchicalstructureswithinwhich

individualsarenestedinneighbourhoodsandcommunitieswiththeirownsetofattributes

thatcanpromoteorantagonizehealthandhealthybehaviours[36–41].Thismixof

individual‐levelobservationsclusteredwithinneighbourhoodswiththeirowndistinct

attributescreatesopportunitiestoquantifythedegreeofinfluenceneighbourhoodfactors

haveonindividual‐levelhealthoutcomesaswellaspotentialinteractionsbetweenthem

[42,43].

153

WepresentamultilevelretrospectivecohortanalysisoftheassociationbetweenGDM

andGHwithPM2.5andSESinBritishColumbia,Canada.Weuserandominterceptlogistic

regressionmodelstoexplorethebetween‐neighbourhoodvariabilityintheriskofGDMand

GHandwhetherinteractionswithPM2.5,neighbourhood‐levelSESindicators,and/or

individual‐levelriskfactorsareabletoexplainanyobservedvariability.

2.0DataandMethodsThiswasapopulation‐basedretrospectivecohortofsingletonbirthsinBritish

Columbiafrom2001to2006(N=237,470).DatafromtheBCPerinatalDataRegistrywere

providedbyPerinatalServicesBritishColumbia(PSBC)whichincludedinformationon

maternal‐infanthealthstatusandoutcomes,reproductivehistory,maternalriskfactors,

attributes,andresidentialpostalcodes.TheRegistryaccountsfor99%ofbirthsand

stillbirthsinBCofatleast20weeksgestationoratleast500gramsbirthweight.Research

dataaccessisprovidedbyaPartnershipAccord/MemorandumofAgreementbetweenall

BCHealthAuthoritiesandPSBCthroughtheFreedomofInformationandPrivacyProtection

Act[44].ResearchethicsboardapprovalwasgrantedbytheUniversityofVictoria(ethics

protocol#:11‐043).

Thetwooutcomevariablesweregestationalhypertension(GH)andgestational

diabetesmellitus(GDM).ThediagnosisofGH(inisolationofproteinuria)istheindication

thatthemotherhadanantepartumbloodpressurereadingofgreaterthanorequalto

140/90ontwoconsecutivereadingsduringthecurrentpregnancy.However,itisimportant

tonotethatthisvariableislimitedtothedegreethatitdoesnotimplythehypertensionwas

inducedbythecurrentpregnancy,andthatthemothercouldhavehadchronic

hypertension.ThediagnosisofGDMistheindicationthatthemotherdeveloped

carbohydrateintoleranceduringpregnancy,whichhasbeencontrolledbydietorrequires

insulintoregulatebloodglucoselevels.ApositivediagnosisforGDMrequiresaminimumof

twoabnormalreadings(outoffour)intheglucosetolerancetest[44].Thehypothesized

causalpathwaysforbothoutcomesarepresentedinFigure21.

Inordertoavoidpotentialselectionbias[45,46],weincludedallbirths(stillbirthand

live)forallgestationalages(20‐42weeks).Excludedrecordsincludedout‐of‐provinceand

invalidpostalcodes(n=1,096),non‐viablebirthspriorto20weeksgestationandlessthan

500grams(n=14),andmissingPM2.5(n=1,510).Duetothesubstantialamountofmissing

maternalBMIvalues(n=68,407;29%),twosetsofmodelswererunusingcomplete‐case

assessmentandmultipleimputationasasensitivityanalysis.

154

Thespatiallocationofeachbirthrecordwasgeocodedbasedonthelatitude‐

longitudecoordinateofthemother’sresidentialpostalcodeatthetimeofdeliveryusing

GeoRef[47].Birthrecordswererelatedtotheircorrespondingcensusdisseminationarea

(DA)byperformingapoint‐in‐polygonspatialjoinprocedureinArcGIS10.2[48].DAsare

thesmallestgeographicalunitforwhichcensusdataareavailableandrepresent

neighbourhoodblocksrangingbetween200–800people.WhileDAsdonotnecessarily

representexistingneighbourhoodcommunities[49],theycanactasproxiesforageneral

catchmentareaofpersonalhome‐lifeactivities[50,51].Birthrecordswereidentifiedas

beingeitherruralorurbanusingtheStatisticsCanadaMetropolitanInfluenceZone(MIZ)

codeswhicharebasedoncommutingflowsofsmalltownsintolargercitiesand

metropolitanareas[52].

ExposuretoPM2.5wasestimatedusinganationalland‐useregression(LUR)model

developedtoestimatePM2.5atthecensusstreetblock‐facelevel[53].Themodeluseda

numberofpredictorsincludingsatellitemeasures,proximitytomajorroadsandindustryto

accountfor46%ofthevariabilityinmeasuredannualPM2.5concentrations.Unlikenitrogen

dioxide(NO2),PM2.5tendstohaveamorehomogeneousintra‐urbandistributionbetween

personal,indoorandambient[54].TheLURmodelestimatesusedforthisstudyshowed

verylittlevariabilityofPM2.5exposuresbetweenindividualswithinagivenDA.We

thereforeaggregatedthepoint‐levelestimatesofPM2.5totheirDA‐levelmeanandrelatedit

toindividualbirthrecordsasanarea‐levelvariable.

TheDA‐levelSESanddemographicdatawererepresentedbythreerelatedbut

independentdatasetsallbasedonthe2006StatisticsCanadanationalcensus.Thefirstwas

aCanadianSESindex(SESi)developedbyChanetal[55].Thesecondwasaneducation

variablerepresentingtheproportionofpopulationover15withanypost‐secondary

education,includingcollege,trades,oruniversity.Thethirdwastheproportionof

continentalAsianimmigrantsincludingtheMiddleEastandSouthAsia.It’sbeenshownthat

theseethnicpopulationstendtohavehigherratesofGDMand/orGHcomparedtotheir

Caucasiancounterparts[35,56,57].AsianandSouthAsianethnicitiesarewell‐represented

throughoutBC,butparticularlyinconcentratedpocketsthroughoutthemajorurbancenter

ofMetroVancouverwherelevelsofPM2.5arealsohigh.CollinearitybetweentheDA‐level

variableswaseliminatedusingasequentialregressiontechniquetocreateuncorrelatedand

independentvariablesforeducationandimmigrantdensity[58].Theeducationand

immigrantdatawereobtainedbyaccesstoABACUSviatheDataLiberationInitiative[59].

155

Allcontinuousvariableswerestandardizedandcenteredtoeaseinterpretationandaid

modelconvergence.

InordertoavoiddatalossfromruralDAs,imputationformissingSES,education,and

immigrantdensityvalueswasperformed.Takingadvantageofthenestedhierarchical

structureoftheadministrativecensusandhealthboundaries,themeanvalueforalarger

encompassingcensussubdivision(CSD)orlocalhealtharea(LHA)wasimputedfora

nestedDAwithamissingvalue.Therewere1,441valuesimputedin52DAsforSESi(0.6%

offinalN,0.8%ofDAs),and3,170valuesimputedin108DAsforbotheducationand

immigrantdensity(1.4%offinalN,1.7%ofDAs).Sensitivityanalyseswereperformedusing

onlythenon‐missingdata.

Multilevel(randomintercept)logisticregressionwasusedtotestourresearch

questions.Thisallowedustoaccountforandmeasurethedegreeofclusteringof

individuals(level‐1)belongingtoagivenDAneighbourhood(level‐2),aswellasquantify

thebetween‐areavariabilityindiseaseriskbeforeandafteradjustmentforlevel‐1and

level‐2riskfactors.Thisexampleofageneralizedlinearmixedmodelallowstheinterceptto

actasarandomparameterwitheachDAhavingitsowninterceptthatvariesfromthe

overall(BC‐wide)intercept.Anyexplainedvariabilitycanthenbeinvestigatedand

measuredwiththeadditionoflevel‐1andlevel‐2variablesandtheirinteractions[60].We

followedabottom‐upapproachtomodelbuildingtoquantifytheexplainedproportional

changeinvariance(PCV),themultilevelmodelequivalenttoanR2[43].Westartedwithan

empty(null)randominterceptmodelwithoutanyindependentvariablesinwhichthe

probabilityofGHorGDMisonlyafunctionofthemother’sresidentialDA.Thepresenceof

significantrandominterceptvarianceindicatesthereareunexplaineddifferencesbetween

neighbourhoodsintheriskofGHandGDM.Wecalculatethemedianoddsratio(MOR)to

translatethearea‐levelvariancetotheoddsratioscalewhichpermitsthedirect

comparisonofitsmagnitudetothatofthelevel‐1andlevel‐2factors[61,62].TheMOR

dependsdirectlyonthearea‐levelvarianceandisdefinedasthemedianoddsratiobetween

anytwoareaspickedatrandomwithdifferingrisk(i.e.whatisthemedianincreaseinrisk

foranindividualmovingtoanareawithahigherrisk).

SubsequentmodelsincludedtheindividualandDA‐levelvariablesalongwithcross‐

levelandwithin‐levelinteractionsinordertoassesstheirfixedeffectsontheoutcomes.

Specifically,wewereinterestedintwoeffectinteractionmodels.First,doestheassociation

ofPM2.5onGDMandGHdifferbySESstatus;andsecond,isthereeffectheterogeneityby

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BMIstatus.Modelswerethencomparedtodeterminehowmuchtheinclusionofcertain

setsofvariablesaddressedanyunexplainedinterceptvariance.Modelsweretestedusing

theAkaikeInformationCriterion(AIC)toevaluatemodelperformance.Allstatistical

analyseswereconductedinStata13IC[63].

Weconductedseveralsensitivityanalysestotesttherobustnessoftheresults.First,

todetermineifthemissingBMIdatabiasedmodelestimates,multipleimputationwas

performed[64].Wewerealsointerestedifmaternalleveleducationwouldattenuatethe

DA‐leveleducationandSESassociations.ImputedChainedEquations(ICE)wereusedto

impute68,151missingBMIvaluesand205,889missingmaternaleducationvalues.All

covariates,exposureandoutcomevariableswereincludedintheimputationprocess,and

twentysetsofmissingvalueswereimputedafteraninitialburn‐inof10iterations[65].We

usedlogisticregressionwithrobuststandarderrorstoestimatethemodelswithinthe

multipleimputationframeworktoaccountforclusteringofindividualswithinDAs.

Second,weusedabnormalglucosefactor(AGF)asanindicatorofpre‐GDMandtest

itsassociationwithPM2.5andSESvariables.AGFindicatesthepossiblepresenceof

gestationaldiabetesinpregnancy,withoutconfirmationofdiagnosis(e.g.themothermay

haveoneabnormalglucosetolerancetest,carbohydrateintolerance,butadiagnosisof

diabeteshasnotbeenmade)[44].Lastly,weusedtestsforspatialautocorrelation(local

Moran’sIstatistic)tocheckforresidualspatialautocorrelationintheresidualsthatwould

indicatetheexistenceofunobservedspatialprocessescausingDAstoclusterandasignof

possiblemodelmisspecification[66].Wethenranspatiallagregressionmodelstoquantify

themagnitudeofanyremainingspatialdependenceusingthelevel‐1adjustedrandom

interceptresidualsastheDA‐leveldependentvariable.Duetothepresenceofislands(DAs

withnoneighbours),weusedanearestneighbourof7(KNN=7)spatialweightmatrixasit

wasthemostfrequentneighbouramountandproducedthehighestMoran’sIvalueswhen

testingmodelrandomeffects(comparedtoKNNof4,5,6,8,10,15).PredictionoftheDA‐

levelrandomintercepterrorusedanEmpiricalBayesmethodavailableasapost‐estimation

commandinStata13IC[67].

3.0ResultsDuetodifferencesintheexclusioncriteria,thetotalnumberofbirthsforGHandGDM

differed.Therewere166,369and165,727singleton(liveandstillborn)birthsforGHand

GDMrespectivelylocatedin6,312neighbourhoodDAs(min.=1,max.=698/694,avg.=

26).Table13summarizestheuntransformedindividualandneighbourhoodcovariates

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(non‐centered,non‐standardized).Oldermaternalage,largerBMI,andhigherPM2.5were

notablyhigherinbothGHandGDMcases;whereasruralresidencehadanotablelower

proportionofGHandGDMdiagnoses.Table14reportstheadjustedoddsratios(ORs)with

95%confidenceintervals(95%CI)forGHandGDMinrelationtothePM2.5quintilesaswell

astheDA‐levelvariables.Inadditiontotherandomintercept(RI)modelestimates,the

resultsfromthemultipleimputed(MI)estimationarealsoreported.Theresultsshowa

similardose‐responseassociationbetweentheriskofbothGHandGDMwithincreasing

PM2.5concentrations.HigherimmigrantdensityalsoshowedanincreasedriskforbothGH

andGDM,whilehigherSESandDA‐leveleducationshowedprotectiveassociations.The

multipleimputedmodelforGH(GH‐MI‐m1)showedaslightattenuationintheORsforPM2.5

andimmigrantdensity,butremainsignificant.Conversely,themultipleimputedmodelfor

GDM(GDM‐MI‐m1)showedaslightincreaseintheORsforPM2.5butareattenuated(shift

towardone)fortheotherDA‐levelvariables.Theimputationandinclusionofmaternal‐

leveleducationmayhavehadanattenuatingeffectontheDA‐levelSESandhigher

educationinthemodelforGDMmoresothanforGH,butbothremainsignificantpredictors

intheirrespectivemodels.TheresultsaregraphicallypresentedinFigure22.

Figure21:DirectedAcyclicGraphsdepictingthehypothesisedrelationshipsforGHandGDM

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Table13:Summaryofpopulationandneighbourhoodcharacteristics,[n(%)]

Characteristic GH3,918(2.4)

Total(n=166,369)

GDM11,306(6.8)

Total(n=165,727)

Level‐1(individual) Nulliparous 2,580(65.9) 80,359(48.3) 6,136(40.7) 106,325(45.5)

Maternalage

<=24years 632(16.1) 28,485(17.1) 758(6.7) 28,415(17.2)

25–35years 2,196(56.1) 102,497(61.6) 6,681(59.1) 102,148(61.6)

>=36years 1,090(27.8) 35,387(21.3) 3,867(34.2) 35,164(21.2)

BMI

UnderWt(<18.5) 121(3.1) 10,323(6.2) 459(4.1) 10,318(6.2)

NormalWt(18.5–24.9) 1,714(43.8) 104,112(62.6) 5,385(47.6) 103,904(62.7)

OverWt(25–29.9) 1,056(27.0) 33,570(20.2) 2,879(25.5) 33,378(20.1)

Obese(>=30) 1,027(26.2) 18,364(11.0) 2,583(22.9) 18,127(10.9)

GestationalDiabetes 535(13.7) 11,306(6.8) ‐‐ ‐‐

Pre‐existingDiabetes 74(1.9) 642(0.4) ‐‐ ‐‐

GestationalHypertension ‐‐ ‐‐ 535(4.7) 3,844(2.3)

DrugorAlcoholFlag 90(2.3) 3,679(2.2) 131(1.2) 3,666(2.2)

Smokedduringpregnancy 363(9.3) 17,519(10.5) 874(7.7) 17,437(10.5)

FallorWinterSeason 1,847(47.1) 80,152(48.2) 5,606(49.9) 79,836(48.2)

Level‐2(DA)Variables

SESi* ‐1.57–1.12 ‐1.36–1.16 ‐1.64–1.04 ‐1.36–1.16

HigherEducation* 0.36–0.66 0.36–0.66 0.36–0.58 0.35–0.66

ImmigrantDensity* 0–0.50 0–0.47 0–0.52 0–0.47

RuralAddress 257(6.6) 13,920(8.4) 416(3.7) 13,871(8.4)

OnFirstNationReserve 29(0.7) 1,694(1.0) 74(0.7) 1,687(1.0)

PM2.5quintile Q1 595(15.2) 33,257(20.0) 1,491(13.2) 33,124(20.0)

Q2 659(16.8) 33,277(20.0) 1,765(15.6) 33,145(20.0)

Q3 752(19.2) 33,277(20.0) 2,419(21.4) 33,158(20.0)

Q4 949(24.2) 33,268(20.0) 2,683(23.7) 33,138(20.0)

Q5 963(24.6) 33,290(20.0) 2,948(26.1) 33,162(20.0)

Pre‐existingDiabetes:indicationofpre‐existingdiabetesmellitus(Type1or2)orinsulin‐dependentdiabetesmellitus;DrugorAlcoholFlag:physicianindicateduseofdrugs(prescription,non‐prescription,illicit)oralcoholasariskfactorincurrentpregnancy;Smoked:indicationthatthemothersmokedatsomepointduringthepregnancy;Fallorwinterseasonofbirth:Sept–Feb;HigherEducation:proportionofpopulationwithanypost‐secondaryeducation;ImmigrantDensity:proportionofpopulationidentifiedasimmigrantsfromcontinentalAsia(includingMiddleEastandSouthAsia).*showthe10thand90thpercentiles.

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Table14:ORsforGHandGDMinrelationtoPM2.5andDA‐levelSESvariables GH‐RI‐m1

N=166,369OR(95%CI)

GH‐MI‐m1N=234,776OR(95%CI)

GDM‐RI‐m1N=165,727OR(95%CI)

GDM‐MI‐m1N=233,842OR(95%CI)

PM2.5quintile

Q1 1.00(reference) 1.00(reference) 1.00(reference) 1.00(reference)

Q2 1.18(1.05–1.33) 1.12(1.00–1.25) 1.17(1.08–1.27) 1.23(1.14–1.33)

Q3 1.32(1.17–1.49) 1.18(1.06–1.33) 1.39(1.28–1.50) 1.43(1.33–1.55)

Q4 1.69(1.51–1.91) 1.50(1.34–1.67) 1.54(1.42–1.68) 1.57(1.45–1.70)

Q5 1.69(1.50–1.91) 1.61(1.44–1.81) 1.63(1.50–1.78) 1.74(1.61–1.89)

SESi 0.91(0.88–0.94) 0.94(0.91–0.98) 0.78(0.76–0.80) 0.84(0.82–0.86)

HigherEducation 0.95(0.92–0.99) 0.94(0.91–0.97) 0.91(0.89–0.94) 0.95(0.93–0.98)

ImmigrantDensity 1.12(1.09–1.16) 1.09(1.06–1.12) 1.17(1.15–1.20) 1.15(1.13–1.18)

DA‐meanBMI 1.04(1.00–1.09) ‐‐ 1.09(1.06–1.12) ‐‐

RuralResidence* ‐‐ ‐‐ 0.56(0.49–0.62) 0.57(0.51–0.64)

TheORsforthethreecontinuousDA‐levelvariablescorrespondtoa1standarddeviationchangefromthemean.ModelsforGHadjustedfor:maternalage,nulliparous,pre‐existingdiabetes,BMI,maternalsmoking,drugflag;ModelsforGDMadjustedfor:maternalage,nulliparous,BMI,drugoralcoholflag,FirstNationon‐reservebirth;MI‐modelsadditionallyadjustedforimputedmaternaleducationandimputedBMI,butnotDA‐meanBMI.*Duetothecorrelationbetweenrural,PM2.5andSES,theORsreportedherearethedirecteffectsofruralresidenceonGDMconditionalontheothervariablesinthemodel.RuralwasnotsignificantforGH.

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Figure 22: Adjusted ORs and 95% CIs for GDM and GH in relation to PM2.5 quintiles and DA-level SES variables

Table15reportstheresultsfromtheinteractionmodelbetweenPM2.5,SESandrural

residenceonGDMforboththerandominterceptandmultipleimputedmodels.Theresults

showthatthedose‐responseassociationbetweentheriskofGDMandincreasinglevelsof

PM2.5ispresentonlyforurbanbirthsandthattheprotectiveassociationofSESonGDMis

alsolesspronouncedinruralareas.TheseresultsaregraphicallypresentedinFigure23

whichclearlyshowsthestarkcontrastineffectsbetweenurbanandruralbirths.The

multipleimputedmodelshowsslightchangesinthemagnitudeoftheORs,butdoesn’talter

theirdirectionorinterpretations.

Table16reportstheresultsfromtheinteractionmodelbetweenPM2.5,SESand

maternalBMI(aboveorbelowBMI=25).TheresultsaregraphicallypresentedinFigure24

whichshowthatlivinginhigherSESneighbourhoodsreducestheriskofGDMamonghigher

BMImothersaswellasbeingprotectiveagainstincreasedlevelsofPM2.5.Theinteraction

betweenBMIandPM2.5suggeststhattheeffectofPM2.5onGDMisstrongeramonglarger

BMImothers,butonlyamongthoselivinginlowerSESDAs.

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Table15:ORsforgestationaldiabetesinrelationtoPM2.5,SES,andRuralResidence

GDM‐RI‐m2

SES*Rural*PM2.5OR(95%CI)

GDM‐MI‐m2SES*Rural*PM2.5OR(95%CI)

PM2.5quintile Q1 1.00(reference) 1.00(reference)Q2 1.32(1.19–1.42) 1.40(1.27–1.55)Q3 1.59(1.44– 1.67) 1.72(1.57–1.88)Q4 1.72(1.55– 1.79) 1.85(1.68–2.03)Q5 1.83(1.65– 1.92) 2.05(1.87–2.26)SESi 0.93(0.85–1.02) 0.93(0.86–1.02)RuralResidence 0.71(0.59–0.84) 0.75(0.63–0.89)Rural*PM2.5quintile Rural*Q1 1.00(reference) 1.00(reference)Rural*Q2 0.79(0.60–1.05) 0.75(0.58–0.98)Rural*Q3 0.62(0.42–0.91) 0.65(0.47–0.90)Rural*Q4 0.58(0.33–1.05) 0.53(0.35–0.81)Rural*Q5 0.52(0.28–0.97) 0.63(0.46–0.86)SESi*PM2.5quintile SESi*Q1 1.00(reference) 1.00(reference)SESi*Q2 0.84(0.75–0.94) 0.91(0.82–1.02)SESi*Q3 0.89(0.80–0.99) 0.91(0.83–1.01)SESi*Q4 0.79(0.72–0.88) 0.85(0.77–0.93)SESi*Q5 0.82(0.74–0.91) 0.86(0.78–0.94)Rural*SESi 0.84(0.72–1.00) 0.87(0.76–1.00)Modeladjustedfor:maternalage,nulliparous,BMI,drugoralcoholflag,DA‐leveleducation,DA‐levelAsianimmigrantdensity,FirstNationon‐reservebirth;DA‐meanBMI;Multipleimputedmodeladditionallyadjustedfor

Figure23:PredictedProbabilityofGestationalDiabetesMellituswith95%CIsinrelationtoPM2.5,SESandRuralResidence

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Table16:ORsforgestationaldiabetesinrelationtoPM2.5,SES,andmaternalBMIª

GDM‐RI‐m3SES*BMI*PM2.5OR(95%CI)

GDM‐MI‐m3SES*BMI*PM2.5OR(95%CI)

PM2.5quintile

Q1 1.00(reference) 1.00(reference)

Q2 1.32(1.16–1.51) 1.35(1.23–1.49)

Q3 1.70(1.50– 1.93) 1.69(1.54–1.86)

Q4 1.91(1.68– 2.17) 1.87(1.69–2.07)

Q5 2.00(1.76– 2.28) 2.13(1.90–2.38)

BMI(≥25) 2.83(2.52–3.16) 2.69(2.40–3.01)

SESi 0.87(0.79–0.95) 0.87(0.80–0.95)

BMI*SESi 1.06(1.02–1.11) 1.04(1.00–1.09)

DA‐meanBMI 1.12(1.09–1.16) 1.11(1.09–1.14)

SESi*PM2.5

SESi*Q1 1.00(reference) 1.00(reference)

SESi*Q2 0.86(0.77–0.96) 0.95(0.85–1.05)

SESi*Q3 0.92(0.83–1.02) 0.95(0.86–1.05)

SESi*Q4 0.83(0.75–0.92) 0.88(0.80–0.97)

SESi*Q5 0.86(0.78–0.95) 0.90(0.82–0.99)

BMI*PM2.5 0.95(0.92–0.98)†

BMI*Q1 1.00(reference)

BMI*Q2 0.93(0.80–1.08)

BMI*Q3 0.81(0.70–0.93)

BMI*Q4 0.75(0.65–0.86)

BMI*Q5 0.77(0.66–0.88)

ªBMItransformedtodichotomousvariable(overweightorobese,BMI≥25);Modeladjustedfor:maternalage,nulliparous,drugoralcoholflag,Ruralresidence,DA‐leveleducation,DA‐levelAsianimmigrantdensity,FirstNationon‐reservebirth;Multipleimputedmodeladditionallyadjustedformaternaleducation.N=165,727forGDM‐M3‐ri;N=233,842forGDM‐M3‐mi;†interactionusingimputedcontinuousinteractionvariable.

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TheDA‐levelrandominterceptvariance,MOR,explainedproportionalchangein

variance(PVC),modeldiagnostics(AIC)andspatialeffectsarepresentedinTables17and

18forGHandGDMrespectively.InTable17fortheGHmodels,theMORand95%CIforthe

Nullrandominterceptmodelwas1.39(1.30–1.50),indicatingthatarandomlyselected

individualwouldexperience,inmedian,a40%increaseintheirriskofGHmovingfroma

lowriskDAtoahigherriskDA.Theadditionofthelevel‐1covariatesincreasedtheMORto

1.45likelyduetotheincreaseinthelevel‐2randominterceptvariance(0.119to0.151).

IncludingtheDA‐levelSESandPM2.5variablespartiallyaddressedtheDA‐levelvariance,

resultinginacumulativePCVof11.3%and32.1%respectivelycomparedtotheNullmodel.

TheMORwasreducedto1.31(1.22–1.45)forthefinalmodel,signifyingthatagooddealof

significantbetween‐areaheterogeneityintheriskofGHremained.Similartrendswereseen

inTables18fortheGDMmodels,butwithlargerDA‐levelrandomeffects.TheMORand

95%CIfortheNullrandominterceptmodelwas1.62(1.57–1.67)whichwasreducedto

1.38(1.33–1.43)inthefinalmodels.TheadditionoftheSESandPM2.5variablesresultedin

acumulativePCVof46%and55%respectively,aconsiderablereductionintheDA‐level

variance.Theinclusionoftheinteractioneffectsaddressedasmalladditionalamountof

variance;however,asignificantamountofDA‐levelheterogeneityremainedunexplained.

Figure24:PredictedProbabilityofGestationalDiabetesMellituswith95%CIsinrelationtoPM2.5,SESandBMI

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Table17:RandomeffectsandmodeldiagnosticsfromhierarchicallogisticmodelsforGHinBC,CanadaRandomEffects&ModelDiagnostics

NullModel Level‐1Model

Level‐2(+SES)

Level‐2(+PM2.5)

Intercept 0.023 0.008 0.008 0.006RIvariance 0.119 0.151 0.106 0.081

PCV(%) Ref. ‐26.9 11.3 32.1

MOR(95%CI)

1.39(1.30–1.50)

1.45(1.36–1.56)

1.36(1.28–1.49)

1.31(1.22–1.45)

AIC 37094 35155 35033 34936

Moran’sI˟ ‐‐ 0.090 ‐‐ 0.049

RIvariance:randominterceptvariance;MOR:Medianoddsratio;AIC:AkaikeInformationCriterion;PCV:proportionalchangeinvarianceexpressesthecumulativechangeintheDA‐levelvariancebetweenthecorrespondingmodelandNullmodel;Moran’sI:LocalMoran’sIforspatialautocorrelation,allresultsweresignificantp<0.01with999permutationsusinganearestneighbor(knn=7)spatialweightmatrix.

Table18:RandomeffectsandmodeldiagnosticsfromhierarchicallogisticmodelsforGDMinBC,CanadaRandomEffects&Model

NullModel Level‐1Model

Level‐2(+SES)

Level‐2(+PM2.5)

GDM‐M2‐ri(SES‐Rural‐PM2.5)

GDM‐M3‐ri(SES‐BMI‐PM2.5)

Intercept 0.064 0.039 0.042 0.032 0.028 0.026RIvariance 0.255 0.295 0.137 0.115 0.110 0.112

PCV(%) Ref. ‐15.7 46.3 55.0 56.6 56.1

MOR(95%CI)

1.62(1.57–1.67)

1.68(1.63–1.73)

1.42(1.38–1.47)

1.38(1.34–1.43)

1.37(1.33–1.42)

1.38(1.33–1.43)

AIC 81917 77576 76579 76427 76410 76834Moran’sI˟ ‐‐ 0.281 ‐‐ 0.098 ‐‐ ‐‐

SeeTable17captionofabbreviationdefinitions.

Spatialanalyseswereusedasamodeldiagnostictotestforsignificantspatial

autocorrelationofmodelresidualsusingthelevel‐2predictedrandomintercepts.Thelocal

Moran’sIstatisticsreportedinTables17and18fortheLevel‐1modelsindicatethe

presenceoflocalspatialclusteringbetweenneighbourhoods,particularlyforGDM.The

additionoflevel‐2variablesreducedtheMoran’sIsubstantially;however,smallbut

significantclusteringremainedforbothGHandGDM(Figures25and26).Spatiallag

modelsfurtherconfirmedthestatisticalsignificanceoftheDA‐levelvariableswhile

accountingforspatialdependenceinthedependent(y)variable.Astatisticallysignificant

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spatiallagcoefficient(rho,ρ)indicatesthattheinterceptvalueforagivenfocalDAcovaries

withthatofitsneighbours.Spatially‐laggedversionsofPM2.5andSESiwereaddedtothe

spatiallagmodelsforGHandGDMtotesttheinfluenceofthevaluesoftheneighbouring

unitsonthefocalDA.Whilesignificant,theyonlyslightlyreducedtherhocoefficient(0.22

to0.21)indicatingresidualspatialdependenceofthedependentvariablewithits

contiguousneighbours.

Furthersensitivityanalysesusingonlythenon‐imputedDAsshowedveryminor

differencesinmagnitudeofsignificantvariablesinboththeGHandGDMmodels.Asan

additionalsensitivitytest,abnormalglucosefactor(AGF)wasusedasanoutcomeaspre‐

diabetesindicator.ThemagnitudeanddirectionoftheDA‐levelvariablesweresimilarto

thoseforGDMshowninTable14,withtheexceptionofPM2.5.HigherlevelsofPM2.5were

showntohaveastrongdose‐dependentassociationwithriskofAGF,OR(95%CI)Q2vs.

Q1:=1.52(1.30–1.78)andQ5vs.Q1:2.41(2.06–2.82).Similarheterogeneitywaspresent

betweenSES,PM2.5andruralresidenceforAGFasitwasforGDM;however,unlikeGDM,the

interactionbetweenBMI,SESandPM2.5wasnotsignificant.

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Figure25:ClustersandOutliersofLocalizedSpatialAutocorrelationinDA‐level(randomintercept)ResidualsforGestationalhypertensioninB.C.,2001–2006Thismapshowsthelocalizedspatialautocorrelation(clustering)oftherandominterceptresidualsafteradjustmentfortheindividualandDAneighbourhood‐levelvariables.AreasindarkredindicateDAclusterswherethemodelunder‐predictedtheriskofGH,whileareasindarkblueindicateDAclusterswherethemodelover‐predictedtheriskofGH.Thelightershadesofblueandredindicatespatialoutlierswherelowareasarenexttohighandhighnexttolow,respectively.Significancewasestimatedusing99,999MonteCarlosimulations,andareawithoutcross‐hatchingindicateareaswheretheprobabilityoftypeIerrorwaslessthan0.01.

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Figure26:ClustersandOutliersofLocalizedSpatialAutocorrelationinDA‐level(randomintercept)ResidualsforGestationalDiabetesMellitusinB.C.,2001–2006Thismapshowsthelocalizedspatialautocorrelation(clustering)oftherandominterceptresidualsafteradjustmentfortheindividualandDAneighbourhood‐levelvariables.AreasindarkredindicateDAclusterswherethemodelunder‐predictedtheriskofGDM,whileareasindarkblueindicateDAclusterswherethemodelover‐predictedtheriskofGDM.Thelightershadesofblueandredindicatespatialoutlierswherelowareasarenexttohighandhighnexttolow,respectively.Significancewasestimatedusing99,999MonteCarlosimulations,andareawithoutcross‐hatchingindicateareaswheretheprobabilityoftypeIerrorwaslessthan0.01.

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4.0DiscussionThisstudyemployedmultilevelrandominterceptlogisticregressionmodelstoassess

theassociationofPM2.5andSES‐relatedneighbourhoodfactorsontheriskofGHandGDM

inBCfortheyears2001to2006.ItisimportanttonotethattheGHvariableusedinthis

studyonlyidentifiespregnancieswherehypertensionwasmeasured,regardlessoftiming

ofonset(i.e.previouschronicorgestational).Regardless,ourresultsshowaconsistent

dose‐responseassociationintheriskofGHandGDMwithincreasinglevelsofPM2.5.Higher

DA‐levelSESandeducationwereassociatedwithlowerrisksforbothGHandGDM,while

higherimmigrantdensityandhigherDA‐meanBMIshowedanincreasedrisk.However,

onlyGDMshowedconsiderableeffectheterogeneity,particularlyinurbanareaswherethe

interactionbetweenPM2.5andSESgreatlymodifiedtheriskofGDM(Figure23).

Furthermore,theseassociationsarepotentiallymorepronouncedamongmotherswith

largerpre‐pregnancyBMI(Figure24).Theuseofrandominterceptmodelsrevealedthat

therewasamoderatedegreeofindividual‐levelclusteringwithinneighbourhoodDAsand

significantbetween‐DAvariabilityintheriskofGHandGDM.WhilethisDA‐levelintercept

variancewaslargelyexplainedwiththeinclusionoftheDA‐levelSESandPM2.5variables,

somevarianceremainedandwasshowntobe,atleastpartially,spatiallyclusteredatalocal

scale.ThislatterobservationsuggeststhepresenceofotherunmeasuredDA‐levelvariables

thatcouldbecausingtheinterceptresidualstocluster.

Ourresultscorroboratethegrowingliteraturesupportingtheassociationbetween

PM2.5exposureandtheriskofGHandGDM[9,10,13–15].Studiesthattesteddifferentair

pollutants(NO2)orusedproximitytoroadsastheexposuremeasurealsofoundsignificant

risksofGDMandhypertensivedisorders,includingpreeclampsia[11,68–70].Wealsofound

anassociationbetweenPM2.5andthepre‐diabeticindicatorofabnormalglucosefactor

(AGF),aresultfoundbyFleischetalaswell[71].WhilethelinkbetweenPM2.5andnon‐

pregnancyhypertensionandcardiovascularinjuryiswellestablished[21,72],thereis

mountingevidenceofanassociationbetweenairpollution(NO2andPM2.5)andtypeII

diabetes[16,27,29,73].Luietal[27]reviewedthehypothesizedmechanismsofPM2.5

mediateddiabetes/insulinresistance,whichhasseveraloverlappingpathwayswiththe

hypothesizedmechanismsofPM2.5mediatedcardiovascularinjuryandgestational

hypertensivedisorders[19,20].Theseincludeendothelialdysfunction,endoplasmic

reticulumstress‐inducedapoptosis,andalteredmitochondrialmorphologyandfunction

largelyinducedbysystemicinflammationandoxidativestress[19,27,74].

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OurresultsshoweffectheterogeneitybetweenPM2.5andSESontheriskofGDMsuch

thatthoseresidinginlowerSESneighbourhoodsshowedamorepronouncedeffectof

increasedlevelsofPM2.5,butonlyinurbanareas(Figure23).Thesignificantinteraction

betweenruraladdressandPM2.5mayreflecttheunderestimationofPM2.5inruralareasby

theLURmodel[53].ThecompositionofPM2.5,andthusitsrelativetoxicity,isshowntovary

spatiallydependingonitssource(e.g.woodsmokevs.traffic‐relatedemissions)andmay

partiallyexplaintheobservedrural‐urbandifferences[19,23].LowerSEShasbeen

associatedwithanincreasedriskofGDMinCanada[3,75],anddisparitiesinbirthoutcomes

byneighborhoodindicatorsofSESareoftenmorepronouncedinurbanversusruralareas

duetogreaterincomeinequality[76,77].Otherexplanationscouldbeduetoaccesstocare

andtheunderdiagnosesoftypeIIdiabetesinruralareas[78],orthattheprevalenceofGDM

islowerinruralcomparedtourbanareasinCanada[7,79].

WealsoobservedsignificanteffectheterogeneitybetweenPM2.5,SESandmaternal

BMI(Figure24).TheseresultsshowthattheeffectofPM2.5ontheriskofGDMisgreateron

motherswhohadapre‐pregnancyBMIof25andabove,butonlyamongthoseinlowerSES

neighbourhoods.Thissuggeststhatnotonlycanneighbourhoodcharacteristicsinfluence

theriskofGDMbutcanalsomodifyexposureeffectsandareconditionalonmaternal

characteristics.It’sbeenshownthatobesitymayenhancetheassociationsbetweenPM2.5

andsystematicinflammation[80],thereforemotherswithlargerpre‐pregnancyBMIsmay

bemoresusceptibletotheeffectsofairpollutionwhencompoundedbyother

neighbourhood‐levelstressors.

Theapplicationofmultilevelmodelsinperinatalepidemiologyhasbecomemore

common,demonstratingtheimportanceofneighbourhoodandcommunity‐levelcontextin

thehealthofindividuals[38,81].Bypermittingtheintercepttovaryrandomlybetweenthe

neighbourhood‐levelunits,itispossibletoquantifythemagnitudetheneighbourhood‐level

factorshaveonaddressingthebetween‐areavariability.Furthermore,specifictologistic

multilevelmodels,computingthemedianoddsratio(MOR)permitstheexpressionofthe

neighbourhood‐levelvarianceontheORscaletherebypermittingthedirectcomparisonof

themagnitudeofthearea‐levelvariancewiththatofthemodelcovariates[62].For

example,inthepresentstudytheMOR(95%CI)fortheLevel‐2modelforGDMadjustedfor

theSESvariablesandPM2.5was1.38(1.34–1.43),signifyingthatinthemediancasethe

residualheterogeneitybetweenDAswillincreasetheriskofGDMby38%whenrandomly

selectingtwopeopleindifferentareas.Therefore,aperson’sresidentialneighbourhoodis

170

ofgreaterrelevancetotheirriskofGDMthansomematernal‐levelfactorsandonparwith

someDA‐levelfactorssuchasDA‐meanBMI,immigrantdensity,andbeinginthe3rd

exposurequartileforPM2.5.Despiteexplainingasubstantialproportionofthebetween‐DA

variance,thereremainedunmeasuredneighbourhood‐levelprocessesproducingbetween

neighbourhooddifferencesinGHandGDMrisk.

WeusedspatialanalysestoexaminewhethertheremainingDA‐levelheterogeneity

wasclusteredspatiallyatthelocalscaleaswellasaservingasameasureofmodel

specificationandhowwellthechosenrepresentativeneighbourhood(DA)unitperformed.

WhiletheinclusionoftheDA‐levelvariablessubstantiallyreducedthespatial

autocorrelationintherandominterceptvariance(Moran’sIinTables17and18),there

remainedsomesignificantspatialautocorrelationforbothGHandGDM(Figures25and

26).Thiscouldsuggestthatsomeunmeasuredspatialprocessisoccurringatthelocallevel,

misspecificationwithrespecttoanunmeasuredcovariateriskfactor,and/ortheuseofDAs

astheneighbourhoodspatialunitbeinglessoptimaltoassesstheneighbourhood‐level

effects.Testingthislastpoint,wefoundthatspatiallylaggedversionsofPM2.5andSESwere

significantinspatiallagregressionmodelsforGHandGDMrespectively.Thissuggeststhe

possiblepresenceofspatialexternalitiesinwhichneighbouringvaluesofthesevariables

influencetheriskofGHorGDMinthefocalneighbourhood[82,83].

Akeycomponentofthisresearchwastheuseofaland‐useregression(LUR)modelof

airpollution[53].WhiletheLURmodelwasindependentlyvalidatedandachieveddecent

overallresultsinitspredictedestimates,theverynatureofourstudydesignensuressome

degreeofexposuremisclassificationtoourstudypopulation,althoughitisexpectedtobe

non‐differential.Theselimitationsincludetheoccurrenceofmaternalintra‐urban

commutingandpotentialinter‐urbanrelocationwithinthepregnancyperiod,thecross‐

sectionalnatureofthePM2.5LURmodelbasedon2006airqualitymonitoringdatawhilethe

studyperiodofourperinataldatasetspans6years(2001to2006)and,finally,thattheLUR

PM2.5concentrationsmaybeunder‐predictedwithhavelessvariabilitycomparedto

compiledmonitoredairqualitydata[84].

Thestrengthsofthisstudyincludealargepopulation‐basedcohortinordertotestfor

potentialeffectheterogeneity,goodcontrolformaternal‐levelconfounderssuchas

smoking,pregnancyhistory,andBMI,andtheabilitytolinktoandassessneighbourhood‐

levelsocioeconomicanddemographiccontextualfactorsthatinfluencethedisease

outcomes.Furthermore,theuseofmultipleimputationmodelsforthemissingBMIaswell

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asmaternaleducationdatafurthersubstantiatedthemainfindings.Thecalculationofthe

MORfromtherandominterceptvariancetermanduseofspatialanalysesshowthatlocal

neighbourhoodfactorsareimportantinreproductivehealthandfurthereffortsto

understandtheirroleshouldbeprioritized.

5.0ConclusionsThisstudysupportsthegrowingliteratureoftheeffectofPM2.5ontheriskofGHand

GDMandprovidesnewinsightsintotheroleneighbourhood‐levelSESanddemographic

factorsplay.WithrespecttoGDM,higherSESneighbourhoodshadastrongprotectiveeffect

againstPM2.5exposureinurbanareaswhileatthesametimewerealsoshowntotemper

thenegativeeffectoflargermaternalBMIsonGDMrisk.Theuseofspatialanalyses

indicatesthatunmeasuredlocalizedprocessesareresponsiblefortheunexplained

between‐neighbourhoodheterogeneity,andthecommunicationusingriskmapsmay

informpublichealthandmunicipalplannersonpossiblemechanismstotarget

improvements.TheimplicationsofapotentiallinkbetweenGHandGDMwithPM2.5

exposureareprofoundasthisairpollutantispervasivethroughouttheworld,andtherefore

evenmodestmitigationinexposurewillprovidesubstantialpublichealthbenefits.

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Chapter7:Conclusions

1.0IntroductionOnOctober1,2015,theInternationalFederationofGynecologyandObstetrics(FIGO)

announceditsground‐breakingpolicystatementonenvironmentalhealthregardingthe

needtoreduceandpreventthereproductivehealthimpactsfromexposuretotoxic

environmentalchemicals[1].Astheleadingvoiceofreproductivehealthprofessionals

worldwide,thisstancebyFIGOhasprofoundglobalresonance.

Theobjectiveofthisdissertationwastoexaminehowfactorsofthesocialand

physicalenvironmentinfluencetheriskofadversepregnancyoutcomesandwhetherthey

interactwitheachotherorwithmaternalcharacteristicstomodifydiseaserisk.Each

researchchapterinthisthesiswasdesignedforindependentpublicationsandcontains

specificconclusions,contributionsandlimitations.Therefore,thisfinalchapterservesto

synthesizetheoverallfindingsandkeycontributions,highlightoveralllimitationsofthe

research,andproposefutureresearchdirections.

2.0SummaryofResearchandContributionsThisdissertationcontributedtotheliteratureaswellastopublicandpopulation

healththrough:(1)theuseofheavymaternalsmokingduringpregnancyasamarkerfor

unmeasuredSES‐relatedlifestyleriskfactorsthatinfluencebirthoutcomes;(2)creating

newepidemiologicalevidenceregardingthevariabilityintheeffectofmaternalsmokingon

birthweightandhowmaternalandneighbourhood‐levelfactorscanexasperateor

attenuateitseffect;and(3)corroboratingthegrowingepidemiologicalknowledge

concerningPM2.5exposureandadversepregnancyoutcomesanditsinteractionwith

neighbourhoodSESanddemographicvariables.

2.1.HeavysmokingasamarkerforunmeasuredSES‐relatedlifestyleriskfactorsMaternalsmokingremainsoneofthemostimportantmodifiableriskfactors,with

potentialimplicationsonsubsequentchilddevelopmentandhealth.Theresultsofthislarge

population‐basedstudyfindthatsmokingduringpregnancyisamodifiabledose‐dependent

riskfactorofadversefetalgrowththatalsohasastrongrelationshipwithotherrisk

behavioursandindicatorsoflowSES.Theseresultsalsoreinforcetheimportanceofthe

standardizedandcompletecollectionofSESvariablesforallpatientsbyperinatalhealth

careproviders.Theimpactofthispaperonthepublichealthliteratureisnotable,being

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designatedasa“highaccess”articleontheBMCPublicHealthwebsiteandbeingreferenced

45timesinotherpeer‐reviewedandotheracademicworks.

Comparedtoalllowerlevelsofsmoking,heavysmokers(≥10cigarettes/day)had

substantiallyworsebirthoutcomesandwerealsoatincreasedrisktobeflaggedforalcohol

ordruguse,beasingleparent,attendfewerprenatalcarevisits,andtohaveapre‐

pregnancyweightgreaterthan74kilograms.Heavysmokerswere3.8timesmorelikelyto

havenotgraduatedhighschoolcomparedwithmoderate,lightandnon‐smokerscombined.

Furthermore,theadditionofthebehaviouralandSESindicatorvariables,particularly

maternaleducation,explainedsomeoralloftheriskattributedtolightsmokingbutheavy

smokingremainedastrongmarkerofincreasedriskforimpairedfetalgrowth.

Thissupportsthepossibilitythatreportsofsmokinggreaterthantencigarettesper

daymightbeanearlymarkerfortheneedforcomprehensivesupportstoreduceadverse

outcomes.InBC,thiswouldidentifyapproximatelyfivepercentofwomenatparticular

increasedriskofadverseoutcomesthatmaybenefitfromadditionalservicestopromotea

healthypregnancy.Theabilitytorecognizethoseatparticularriskearlyinpregnancyand

providepreventativeprogramscouldhelpachievebetteroutcomesforallexpectant

mothers.Whilestrategiesforsmokingcessationareimportantandsupportedbyourstudy,

theunderlyingissuesthatleadtoadversebirthoutcomesmightnotbeaddressedwitha

narrowfocus(demonstratedbytheresultsinChapter5).Thisinformationmaybeusedfor

planningtargetedinterventionprogramsnotonlyforsmokingcessationbutpotentially

othermaternalsupportservicessuchasnutritionandhealthypregnancyeducationwith

theoverallgoalofoptimizingbirthoutcomes.Antenatalcareisacriticalaccesspointforthe

educationofexpectantmothersregardingahealthypregnancy,particularlyforfirst‐time

mothers,whomaybemoreamenabletosmokingcessationandotherhealthylifestyle

changes.

2.2Effectmodificationofmaternalsmokingonbirthweightbyneighbourhood‐levelfactors

Newandimportantepidemiologicalfindingswerereportedinthisdissertationonthe

abilityofneighbourhood‐levelfactorstomodifythenegativeeffectsofmaternalsmokingon

birthweight.

Theassumptionthatmaternalsmokingaffectsallpregnanciesequally,whilesafein

itself,maynotcaptureimportantinsightsintopossiblecessationinterventions.Theresults

inChapter5showthattheeffectofmaternalsmokingonbirthweightisnotconstantacross

geography,butdependsonthesocialandphysicalenvironment.Theuseofrandom

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coefficientmodelsrevealedneighbourhood‐leveldifferencesinhowmaternalsmoking

negativelyimpactedbirthweightanddemonstratedeffectmodificationbyneighbourhood

andmaternal‐levelfactors.TheinclusionoftheDA‐levelSES,demographicandPM2.5

variablesexplained68.5%oftherandominterceptvariabilityinDA‐meanbirthweight.

However,therandomslopevariabilityofmaternalsmokingonbirthweightwasonly

partiallyexplainedbythecross‐levelinteractionssuggestingthepresenceofother

contextualneighbourhood‐levelfactors.

Ouranalysesconfirmpreviouslyshownmodificationofthesmoking‐birthweight

relationshipbymaternalriskfactors;however,toourknowledgethisisthefirststudyto

showthatneighbourhood‐levelfactorsareabletomodifythisrelationship.Wefounda

significantnegativeinteractionbetweencigarettes/dayandneighbourhood‐levelSESi.This

suggeststhatmaternalsmokingmayhavelittlerelevanceinaffectingbirthweightinvery

lowSESneighbourhoods,butbecomesmoreprominentasneighbourhood‐levelSES

increaseswhenperhapsotherstressorsnegativelyimpactingbirthweightarereduced.

Higherproportionsofneighbourhood‐levelpost‐secondaryeducationhadpositive

interactionwithcigarettes/daysuggestingthatheavysmokersmaybenefitthemostby

livinginhighereducatedneighbourhoods.Explanationscouldbethatsmokingcessation

ratesinpregnancyarehigherinbettereducatedneighbourhoodswherehealthier

behavioursaremorecommon.Neighbourhoodsocialsupportsandtransmissionof

behaviourscouldalsoexplaintheobservedpositiveinteractionwithhigherimmigrant

densityandmayreflectthebufferingeffectofstrongcommunitycohesivenessand

beneficialculturalpractices.Conversely,theobservednegativeinteractionbetweenrural

addressandcigarettes/daycouldbeduetothetransmissionofnegativebehavioursbeing

morecommon,andwherelesssupportforcessationmayleadtosmokingthroughout

pregnancy.

ThebufferingeffectofPM2.5withincreasedlevelsofmaternalsmokingiscuriousbut

couldprovideevidenceforaprotectivepre‐conditioningstressthatactivatesanadaptive

responseandincreasesbiologicalresistancetocigarette‐inducedharms.Hence

interventionsfocusingexclusivelyonindividualbehavioursmayhavelimitedsuccess

withoutaddressingthecontextualinfluencesattheneighbourhood‐level.

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2.3Epidemiologicalfindings

2.3.1TheinteractionbetweenPM2.5andneighbourhoodSESvariablesonbirthweightandmeasuresofadversefetalgrowth.

Themainenvironmentalfactorsexaminedincludeambientparticulateairpollution

(PM2.5),neighbourhoodsocioeconomicstatus(SES),neighbourhoodimmigrantdensity,

neighbourhoodlevelofpost‐secondaryeducationlevelandtheurban‐ruralcontext.The

resultsshowthatindividualandneighbourhood‐levelfactorsarecapableofmodifyingthe

associationbetweenPM2.5exposureandfetalgrowth.Furthermore,throughtheuseof

random‐slopesmodelsweshowthattheeffectofgestationalageonbirthweightcanvary

considerablybetweenneighbourhoods.

TheseresultsshowasmallnegativeinteractionbetweenPM2.5andSESsuchthata

morepronouncedeffectofPM2.5wasseeninlowerSESneighbourhoods;howeverthis

resultappearsforbirthsinurbanareasonly.Theprotectiveinteractionwithhigher

immigrantdensityonPM2.5exposureshowsthatnotonlycanneighbourhood

characteristicsinfluencefetalgrowthbutthattheycanalsomodifyexposureseither

positivelyornegatively.

InteractionsbetweenPM2.5andmaternal‐levelvariablesshowntoreducebirth

weightindependentlyrevealedsomecounter‐intuitiveresults.Whilefurtherstudiesare

neededtoconfirmthesefindings,thepersistenceoftheresultsaftervarioussensitivity

analysessuggestsahypothesisthatsomeindividual‐levelexposuresmayactasapre‐

conditioningstressthatactivatesanadaptiveresponseofincreasedbiologicalresistanceto

similarorotherstressors.AprotectiveeffectofoldermaternalageagainstPM2.5exposure

wasalsoobservedandmaystemfromincreasednutritionalawarenessamongolderwomen

and/ormoresecureincomeandsupportnetworkstherebyreducingpotentialstressand

anxiety.Pregnanciesaffectedbygestationaldiabeteshadsignificantlyhigherbirthweights

asexpectedbutrevealedasharpreductioninbirthweightwithincreasingPM2.5.

Ourresultscorroboratethegrowingliteraturesupportinganegativeassociation

betweenPM2.5andbirthweightinasettingofrelativelylowairpollutionconcentrations.

Thisstrengthenstheevidenceofthelow‐doseeffectsofPM2.5.Furthermore,thisstudy

supportsthegrowingliteratureofaneffectofPM2.5onfetalgrowthanditsmodificationby

bothmaternalandneighbourhood‐levelfactors.Mostnotably,itshowsthatlowerSES

neighbourhoodsmaybemorenegativelyaffectedbyhigherlevelsofPM2.5,butonlyinurban

areas.

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2.3.2PM2.5exposureincreasestheriskofgestationaldiabetesmellitus(GDM)andgestationalhypertension(GH).

ThisstudysupportsthegrowingliteratureofaneffectofPM2.5ontheriskofGHand

GDMandprovidesnewinsightsintotheroleofneighbourhood‐levelSESanddemographic

factorshaveontheirprevalence.Ourresultsshowaconsistentdose‐responseassociationin

theriskofGHandGDMwithincreasinglevelsofPM2.5.HigherDA‐levelSESandeducation

wereassociatedwithlowerrisksforbothGHandGDM,whilehigherimmigrantdensityand

higherDA‐meanBMIshowedanincreasedrisk.Further,higherSESneighbourhoodshada

strongprotectiveeffectagainstPM2.5exposurefortheriskofGDMinurbanareaswhilealso

showntotemperthenegativeeffectoflargermaternalBMIsonGDMrisk.

Themedianoddsratio(MOR)permitstheexpressionoftheneighbourhood‐level

varianceontheORscaletherebypermittingthedirectcomparisonofthemagnitudeofthe

area‐levelvariancewiththatofthemodelcovariates.TheMOR(95%CI)fortheadjusted

level‐2modelforGDMwas1.38(1.34–1.43),signifyingthatinthemediancase,arandomly

selectedindividualmovingfromalowriskDAtoahigherriskDAwillrealizea38%

increasedriskofGDM.Thus,afteradjustingforindividualandneighbourhood‐levelknown

riskfactors,aperson’sresidentialneighbourhoodstillexhibitsagreaterrelevancetotheir

riskofGDMthanmanyofthemeasuredmodelvariables.

Theuseofspatialanalysesindicatesthatunmeasuredlocalizedprocessesare

responsiblefortheunexplainedbetween‐neighbourhoodheterogeneity,andthe

communicationusingriskmapsmayinformpublichealthandmunicipalplannerson

possiblemechanismstotargetimprovements.Theimplicationsofapotentiallinkbetween

GHandGDMwithPM2.5exposureareprofoundasthisairpollutantisubiquitous,and

thereforeevenmodestmitigationinexposurewillprovidesubstantialpublichealth

benefits.

3.0Limitations

3.1UseofDAsasneighbourhoodsWeusedspatialanalysestoexaminewhethertheremainingDA‐levelheterogeneityin

themodelswasclusteredspatiallyatthelocalscalewhichservedasameasureofmodel

specificationaswellashowwellthechosenrepresentativeneighbourhood(DA)unit

performed.Inmostcases,theinclusionoftheDA‐levelvariablessubstantiallyreducedthe

spatialautocorrelationintherandominterceptvarianceindicatingthattheDAsperformed

welltocapturetheunderlyingspatialheterogeneity.However,thereremainedsome

significantspatialautocorrelationforsomeofthepregnancyoutcomes,particularlyGHand

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GDM.Thiscouldsuggestthatsomeunmeasuredspatialprocessisoccurringatthelocal

level,misspecificationwithrespecttoanunmeasuredcovariateriskfactor,and/ortheuse

ofDAsastheneighbourhoodspatialunitbeinglessoptimaltoassesstheneighbourhood‐

leveleffectsfortheseoutcomes.Theadditionofotherneighbourhood‐levelvariablessuch

asgreenspaceandneighbourhoodwalkabilityscorewouldbeapossibleavenuetoexplore

first.

3.2ThePM2.5Land‐useRegressionModelAkeycomponentofthisresearchwastheuseofaland‐useregression(LUR)modelof

airpollution.WhiletheLURmodelwasindependentlyvalidatedandachieveddecent

overallresultsinitspredictedestimates,theverynatureofourstudydesignensuressome

degreeofexposuremisclassificationinourstudypopulation.Ouranalysiswasbasedon

maternalplaceofresidenceatdelivery,andthereforeintra‐urbancommutingandpotential

inter‐urbanrelocationwithinthepregnancyperiodwasnotaccountedforwhichcould

affecttheresults.Time‐activitypatternsshowthatpregnantwomenspendmoretimeat

homeinthelaterstagesofpregnancy,butmobilitypatternsmaydifferbyage,parityand

SES.

AnotherlimitationregardingthePM2.5exposureassessmentisthattheLURmodelis

cross‐sectionalbasedon2006airqualitymonitoringdata,whilethestudyperiodofour

perinataldatasetspans6years(2001to2006).Wethereforeassumeallpregnancieswere

exposedtothesamelevelsofPM2.5fortheirentirepregnancy,regardlessoftheiryearof

birth,basedontheirresidentialDA.Thismethodalsoassumesthatairpollutionlevelshave

remainedrelativelyconsistentwithnointer‐regionalvariationbetweentheyears.AirZone

reportsfromtheBCMinistryofEnvironmentshowthatwhiletheaveragelevelsofPM2.5

acrossBChavegenerallydeclinedovertheyearsfrom2004to2006,inter‐regional

variationhasalsoremainedfairlyconsistentintherateofdecreasewhichwillreducedthe

amountofpotentialexposuremisclassification[2].TheuseofthisPM2.5LURmodelalso

preventstheassessmentofexposurewindowsbytrimester,spatiotemporalstudiesofPM2.5

haveshownlittletonodifferencebetweentrimester‐specificandentirepregnancyeffects

onbirthweight.Finally,themeanPM2.5concentrationsmaybeunderestimatedbytheLUR

modelwithlessvariabilityandmissingseveralhighPM2.5outlierlocationsinBCcompared

tocompiledmonitoreddata.Thiscouldpotentiallyresultinanunderestimationofour

observedassociationofreducedbirthweightwithincreasingPM2.5levels.

183

Althoughdirectcomparisonstootherstudiesaredifficulttomakeduetoour

estimationofnon‐lineareffects,substantiallygreaterdifferencesareseeninmyresults,

especiallyinChapter3.Thisislargelyduetomycontrolfortheproportionof

neighbourhoodAsianimmigrantdensityandtheuseofasequentialregressionprocedure

toremovethecollinearitybetweenimmigrantdensityandPM2.5andSESi.Recallthata

residualizedororthogonalvariableforimmigrantdensity(aswellasproportionofhigher

education)wascreatedinordertodisentanglethedirecteffectsofPM2.5andSESfrom

immigrantdensityshowntobemoderatelycorrelated(r=0.53and0.63respectively).

Whenmodelswererunthatusedthenon‐transformedvariables,theestimatedassociations

betweenbirthweightandPM2.5arereducedbyapproximatelyhalf(‐9.9grams,95%CI=‐

12.8to‐7.1);however,despitethelargechangeineffectsizeforPM2.5onbirthweight,all

relationshipsandinteractionswithotherparametersremainedunchangedandstatistically

significant.ThissmallereffectsizeismoresimilartootherstudiesofPM2.5onbirthweight

(seethesupplementarytablesfor[3,4]).Moremethodologicalworkneedstobedonein

futurestudiesinordertocorrectforcorrelatedconfoundingvariablesinordertoestimate

truedirecteffects.

InarecentpaperbyGehringetal.(2014)thatlookedattheimpactofnoiseandair

pollutiononpregnancyoutcomesinMetroVancouverBCCanada,asub‐regioncoveredby

mystudythatalsousedBCPDRdata,theyfoundthatnoiseexposurewaslargely

responsibleforthenegativeassociationswithtermbirthweightinjointmodelsarguing

thatnoisecouldbethepredominantexposure[3].Theiradjustedmeandifference

associationbetweenPM2.5andtermbirthweightwas‐3.1grams(95%CI=‐5.1to‐1.1),

whereasforthetransportationnoiseexposureitwas‐19.1grams(95%CI=‐22.9to‐15.3).

Combined,theirestimatedmeandifferencesareverysimilartotheassociationspresented

inChapter3.Iftheirconclusionsareshowntobetruebyfurtherresearch,itcouldbethat

ourestimatedPM2.5associationsareconfoundedbyunmeasurednoiseexposure.Inurban

areas,thiseffectwillbeverydifficulttoseparatefromairpollutiongiventhatboththese

exposuresarelargelydrivenbylocalvehicletraffic.Studieswithaspecialfocuson

comparingpopulationslivingclosetochronicpointsourcesofnoisewillbeneededto

disentangletheseeffects.

3.3MissingDataWewereunabletocontrolformaternal‐levelSES,andthereforetheneighbourhood‐

leveleffectestimatesandinteractionscouldreflectindividual‐leveldifferences.Maternal

184

educationisavariableprovidedintheBCPerinatalDataRegistry,butwasonlyavailablefor

10%ofourpopulation.InChapter6,maternaleducationwasincludedintheanalysisusing

astatisticallyacceptedmultipleimputationtechniquewithresultsshowingthatwhile

maternaleducationwasprotectiveinitselffortheriskofGDMandGH,itdidnotalterthe

overallDA‐levelassociations.AsweshowedinChapter3,theadjustmentforsocially‐

patternedbehaviouralriskfactorssuchasmaternalsmoking,suspecteddrugoralcoholuse

andfewprenatalcarevisitswillcontrolforsomeindividual‐levelSESdifferences.

3.4FirstNationsBirthsHealthoutcomeresearchandsurveillanceofAboriginalbirthsinCanadaisacomplex

andchallengingendeavour.FirstNationsinCanadamakeupabout4%ofthepopulation,

butisthefastestgrowingsub‐populationinCanada.FirstNationspeopleareincreasingly

livingincitiesoroff‐reserves,somebychoiceandsomebynecessity.Reproductivehealth

surveillanceisasimportantinFirstNationsofBC,asinmainstreamBC,toensurethat

evidence‐basedpoliciesandsupportsaretargetedtoareaswheretheycanhavethemost

beneficialimpactforhealthymaternalandchildhealthoutcomes[5,6].

ThisresearchwaslargelyfundedbyaCIHRInstituteofAboriginalHealthPriority

initiativealthoughitwaspeerreviewedbytheChildren’shealthcommittee.Thestartofthis

projectoccurredduringthetransitiontotheFirstNationsHealthAuthority(FNHA).Dr.

ArbourandIheldthreemeetingswithvariousrepresentativesfromtheresearcharmofthe

organizationthatlaterbecametheFNHA.Giventhattherewerenospecificprocessesin

placeatthattimeforpopulationepidemiologystudiessuchasours,wewereprovidedwith

theinformationthatindividualFirstNationcommunityapprovalforeverycommunityinBC

wouldberequiredtoobtainFirstNationsidentifiersforBCbirthrecords.Asthiswas

unfeasible,ourresearchcombinedallbirths(FirstNationsandnon‐FirstNations)without

evaluatingFirstNationsspecificrisksastheyrelatedtothesocialandphysical

environment.IthasbeenshownthataftercontrollingforSESandothermaternalrisk

factors,Aboriginalidentityisnotanindependentriskfactorforsomeadversebirth

outcomesordifferencesinriskbetweenFirstNationandnon‐FirstNationbirthwere

greatlyattenuated[7‐9].Ourmethodswillallowforahigherlevelofcomprehensive

analysis,onceprotocolsforresearchinBCareestablishedbytheFNHA.

4.0OverallImplicationsandFutureConsiderationsReproductivehealthandpregnancyoutcomesareimportanthealthmeasurestostudy

forseveralreasons,themostimportantbeingthatexposuresandstressorsinuteroand

185

earlychildhoodcaninfluenceone’sentirelifetrajectory[10‐13].Thefetaloriginsofdisease,

or“BakerHypothesis”,postulatesthatperturbationoftheearlynutritionalenvironment

haslong‐termstructural,physiologicalandneurologicalimpactsonnewbornsthat

predisposethemtochronicdiseasesinadulthoodincludingtype2diabetes,hypertension,

coronaryheartdiseaseandobesity[11,12].Thishypothesishasbeenexpandedtoinclude

excessfetalglucocorticoidexposurefrommaternalsocialstressorsactingontheHPA‐axis

andpotentiallyleadingtoearlylifeprogrammingofdisease[14,15].Theseearly

developmentalexposuresmayelicitepigenomicmodificationsanddysregulationsuchas

DNAmethylationthathavelifelongandpotentiallytransgenerationalimplicationsfor

health[16–18].

Similarly,itisnowwelldemonstratedthatSES‐relatedriskfactorscanalsohavea

life‐coursetrajectorylatencyperiodonhealth[19–22]whichcanleadtothe

intergenerationaltransmissionofpovertyandpoorhealth[23].Thisisparticularlyrelevant

forIndigenouspopulationsinNorthAmericaandelsewherewhohaveenduredcenturiesof

colonizationandon‐goingsystemicandinstitutionallyentrenchedviolence,racismand

marginalization.Theeffectofthistypeofenduringtraumagoesbeyondthelifespanof

individualstopersistinter‐generationallywithinfamiliesandwholecommunities[24–28].

Therefore,theinequitiesofhealthamongnewbornsasaresultofdifferentsocialand

geographicsub‐populationsrepresentanintolerablebutincessantcycleofinjustice.

InCanada,roughly6%ofbirthsareconsideredLBWwhichcorrespondsto

approximately25,000affectedinfantseachyear[29].Itisestimatedthatbetweentwoand

tenpercentofthesecanbedirectlyattributabletoenvironmentalexposuresexcluding

tobacco,alcoholandillicitdruguseamountingtoroughly$1.5millionindirectandindirect

costseachyear[30].However,thisfiguredoesnotcapturethelatentandlong‐termcosts

associatedwithLBWthatdisproportionatelyaffectlowSEShouseholds[31‐33].

Theassessmentofthelong‐termeffectsofbeingbornsmallisdifficulttodiscerndue

totheconfoundingofotherSES‐relatedriskfactors.A26yearfollow‐upstudyofthe1970

BritishBirthCohortfoundthatchildrenbornfull‐termbutSGAbelowthe5thpercentile

demonstratedsmallbutsignificantdeficitsinacademicachievementsatfollow‐upagesof5,

10and16[34].Thesesmalldifferencesinschoolperformancemayhaveledtobeingless

likelytohaveprofessionalormanagerialjobsandlowerweeklyincomeatage26.There

wasalsoasignificantheightdifferentialbetweenSGAandaveragebirthweightparticipants.

However,andperhapsjustasimportantly,therewerenoreporteddifferencesintotalyears

186

ofeducation,employment,hoursofworkperweek,maritalstatus,orsatisfactionwithlife.

Theseresultsremainedsignificantevenafteradjustingforsocialclass,sex,regionofbirth,

andthepresenceoffetalorneonataldistress[34].

Inotherstudiesofshorterfollow‐uptimes,moderateandearlypretermbirth(<30

weeksgestation)andlowerbirthweightswereshowntonegativelyaffectschool

performanceaftercontrollingforparentaleducation,andremainedwhencomparingsibling

pairs[35].However,similarresultsoflowerphysicalsize,cognitivescoresandacademic

achievementatage8wereonlyfoundforthoseSGAinfantswithimpairedpost‐natal

growth(i.e.SGA+failuretothrive)whileSGAinfantswithadequatepostnatalgrowth

showednodifferencecomparedtonormalgrowthchildren[36].Furthermore,whilelow

andverylowbirthweightwerenegativelyassociatedwithacademicperformance,the

growthrestrictedchildrenshowedgreatersensitivitytoparentingtechniquesaround

learningandshowedcompensatoryacademiceffectsunderconditionsofsensitive

parentingexperiences[37].Thesestudiesshowthatbeingbornsmallorpretermdoesnot

necessarilyimplyalifesentenceofstruggleandhardship,butthatthehomeand

communityenvironmentmatters[38–40].Itisnowwell‐establishedthataccessand

proximitytogreen‐spaceshasapositiveeffectontheriskofadversepregnancyoutcomes,

bufferingtheexposuretotraffic‐relatedairpollutionandnoise,andmaythereforebelinked

tootherchronichealthoutcomesinthegeneralpopulationinadditiontopregnantwomen

andtheiroffspring[41‐46].Thisisanareaofincreasingimportanceandmitigation.

ReturningtotheFIGOpolicystatement,theymakefourrecommendations:1)

advocateforexposurereductionpolicies;2)worktoensureahealthyfoodsystemforall;3)

makeenvironmentalhealthpartofhealthcare;and4)championenvironmentaljustice[1].

Researchers,policymakers,healthprofessionalsandcommunityorganizersneedtowork

togethertoaccomplishtheserecommendations.

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Appendix1RiskSurfaceMaps

Spatialepidemiologyislargelyconcernedwiththeanalysisofspatially

continuousdiseaseriskandriskfactors.Asaresult,continuoussurfaceriskmaps

(orisoplethmaps)aredesired.

Thereareseveralmethodsavailableforthespatialinterpolationofregional

dataintoarisksurface.Fortherisksurfacemapspresentedbelow,Ifollowedthe

kriggingmethodologypresentedbyOlafBerke(BerkeO.2004.Exploratorydisease

mapping:krigingthespatialriskfunctionfromregionalcountdata.International

journalofhealthgeographics.2004,3(18)).Here,twosophisticatedmethodsare

combinestocreatecontinuousspatialriskmapsofadversepregnancyoutcomes.

First,empiricalBayesestimationisusedtosmooththespatialriskestimatesto

stabilizethestandarderrorsandreducetheimpactofoutliersby“borrowing

strengthfromtheensemble”inwhichunstablevariancesfromsmallareasamples

areshrunktotheglobalvariancebypoolinginformationacrossthestudyregion.

Theprocesscouldbeseenasrelatedtointernalstandardizationinepidemiology.

Second,ordinarykrigingwasusedtopredict(interpolate)thesmoothedrates

intoacontinuousrisksurface.Inordertoremovethepotentialforover‐smoothing

thatkriggingwouldperformbymodelingthesmallscalespatialvariability,the

semivariogramwasmodelledwithoutnuggeteffectwhichleadstothedirect

interpolationatthesamplingsites(i.e.theDAswithknownrates)andsmoothed

predictionsattheunknownsitesshrunktowardstheglobalmeanvalueofthe

estimatedtrendsurface.Theresultingmapsareeasilyinterpretabletomapusers,

includingotherresearchersandpolicymakers.

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Map1

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Map2

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Map3

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Map4

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Map5

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Map6

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Map7

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Map8

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Map9

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Map10

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Map11

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Map12

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Appendix2AdditionalFiguresforChapter4

DirectionalAcyclicGraphicofhypothesizedcausalprocess,whereE:PM2.5exposure,D:birthweight,PTB:pretermbirth,gdiab:gestationaldiabetes,htn:gestationalhypertension,Z:level‐2orneighbourhood‐levelvariables

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CaterpillarplotoftheDA‐levelrandominterceptscomparingtheLevel‐1modelwithonlyindividual‐levelvariables(A1&B1)totheLevel‐2modelcontainingDA‐levelvariables(A2&B2)fortwodifferentHealthServiceDeliveryAreas.TheLevel‐2modelsexplainasubstantialproportionofthebetween‐DArandominterceptvarianceinbirthweight

205

ExampleofthevariabilitybetweentherandomslopesforgestationalageonbirthweightfortheneighbourhoodDAswithinagivenregionalhealthauthority(VancouverIsland)

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Appendix3ResultsforSGA‐3,SGA‐10,IUGR,TermLBW,andPTB

BCspecificbirthweightreferencecharts[1]wereusedtoidentifybirthsthatwere

small‐for‐gestationalagebelowthe3rdand10thpercentileforweightandsex(SGA‐3and

SGA‐10respectively).Afterexclusions,therewere230,903singletonlivebornbirthslocated

in6,338neighbourhoodDAs(min.=1,max.=779,avg.=36).Excludedobservations

included:missingbirthweight(n=46),missinggestationalage(n=373),stillbirths(n=

1,047),missingcigarettes/day(n=2,484),missingPM2.5(n=1,502),missingsex(n=5).

SensitivityanalyseswererunincludingstillbirthsaswellasusingaCanadian‐widebirth

weightreferencetoclassifySGA‐3andSGA‐10[2]withonlyveryminordifferencesfor

somecoefficientsandstandarderrors.

Termlowbirthweight(tLBW)isdefinedasbirthwhichreachedagestationalageof

37completedweeksbuthadbirthweightslessthan2,500grams.Therewere214,178

singleton(livebornandstillborn)birthslocatedin6,337neighbourhoodDAs(min.=1,max.

=709,avg.=34).Excludedobservationsincluded:birthswithamissingorlistedgestational

age<37weeks(18,547),missingbirthweight(n=46),missingcigarettes/day(n=2,204),

missingPM2.5(n=1,381),missingsex(n=4).

Intrauterinegrowthrestriction(IUGR)isapre‐definedBCPDRfieldderivedfrom

birthchartsasbeingphysicianidentifiedIUGRduringtheantenatalperiodusingultrasound

imaginggrowthparameters.Afterexclusions,therewere231,305singletonslivebornbirths

locatedin6,338neighbourhoodDAs(min.=1,max.=780,avg.=37).Excluded

observationsincluded:stillbirths(1,052),missingcigarettes/day(n=2,493),missingPM2.5

(n=1,502),missingsex(n=5).Asensitivityanalysisincludingstillbirthswasnotabletobe

performed;however,resultsarenotexpectedtodiffermuchgiventheoutcomeofsimilar

previousanalyses.

Bothregular(maximumlikelihood)logisticregressionwithrobuststandarderrorsas

wellasrandominterceptlogisticregressionwereusedtocalculateandcomparedresults.

WhilebothtypesofstatisticaltechniquesaccountforthewithinDAclusteringofthe

individualobservations,thedesiretoquantifythevariabilityforthebetween‐DAintercept

wasthereasonforusingtherandominterceptmethods.Comparisonofthecoefficientsand

standarderrorsbetweensimilarmodelspecificationssawonlyveryslightdifferencesin

someofthestandarderrorswhilemostofthecoefficientswerenearlyidenticalforall

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outcomesandmodelstested.Allthefiguresbelowthereforeusetheresultsfromtheregular

logisticregressions.

Figure1showstheoddsratioswith95%confidenceintervals(OR,95%CI)forthe

individualandDA‐levelvariables.Allfourbirthweightoutcomesshowsimilartrendsfor

mostofthevariables.Thetwoexceptionsfortheindividual(level‐1)variablesincludetype

IIdiabetesmellitus(T2‐diabetes)andgestationaldiabetes(Gest.Diabetes),demonstrating

protectiveassociationsforSGA‐3andSGA‐10.Thereasonforthisisunclear.Withrespectto

theDA(level‐2)variables,onlyruralresidencestandsoutforIUGRasdemonstratinga

protectiveassociation.ThiscouldbeadataregistryrelatedissuegiventhatIUGRistheonly

outcomeofthefourwhichisanindependentBCPDRdatafieldandforwhichdata

completenessforruralhealthcentrescanbesub‐optimal(personalcommunicationwith

BCPDRstaff).

Figure1:Adjustedoddsratiosand95%confidenceintervals(95%CI)individualandneighbourhood‐levelvariablesonmeasuresofadversefetalgrowth

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TheobservedORsinFigure1forallfouroutcomesconfirmourfindingsinChapter4

regardingthenegtiveassociationofPM2.5onbirthweightandfetalgrowth.Figure1shows

aconsistentdose‐effectforallfourmeasureofadversefetalgrowth.Theseresultsalso

furtherconfirmthefindingsofSES,higherproportionsofneighbourhood‐levelpost‐

secondaryeducationandthetrendsobservedforneighbourhoodswithhigherdensitiesof

Asianimmigrantpopulations.TheincreasedORsfortheAsianimmigrandensityvariableis

likelynotduetoaninherentpathologicalmechanismoraproxieforanunmeasured

exposure,butlikelyduetotheconstitutionaldifferencesinbirthsize[3,4].

SGA‐3&SGA‐10

Table1showstheresultsforSGA‐3andSGA‐10fromaninteractionmodelbetween

PM2.5,SESandurban‐ruralresidence.Thepredictedprobabilitiesderivedfromthese

modelsarepresentedinFigure2usingtwodifferentvariableperspectives.Whatthese

modelsshowisthatrisingPM2.5alwaysincreasestheriskofSGA,andthatthisriskis

modestlytemperedbyrisingSES,butonlyinurbanareas.Themedianoddsratio(MOR)

translatethearea‐levelvariancetotheORscalewhichpermitsthedirectcomparisonofits

magnitudetothatofthelevel‐1andlevel‐2factors[5,6].TheMORforSGA‐3andSGA‐10

signifythat,inthemediancase,arandomlyselectedindividualmovingfromalowriskDA

toahigherriskDAwillrealizea38%and13%increasedriskofSGA‐3andSGA‐10

respectively.Thus,afteradjustingforindividualandneighbourhood‐levelknownrisk

factors,aperson’sresidentialneighbourhoodstillexhibitsagreaterrelevancetotheirrisk

ofSGAthanmanyofthemeasuredmodelvariables.

Non‐linearspecificationsofPM2.5conditionalonSESandurban‐ruralresidence

weretestedusingaquatradicexpressionofthecontinuousPM2.5variableaswellasits

quintiletransformation.TheformerdidnotshowtobesignificantforSGA‐3,andjust

borderlinenon‐significantforSGA‐10(p=0.07).ThequiniletransformationofPM2.5

showedsomeindicationofnon‐linearityconditionalonSESandurban‐ruralresidence

(Table2andFigure3).Forurbanareasonly,therewasclearmodificationofthePM2.5

associationbySESontheriskofSGA‐3andSGA‐10.Forruralareas,therewasaconsistent

increaseinriskofSGA‐3andSGA‐10withrisingPM2.5levels,butshowednostatistically

significantdistinctionacrossthelevelsofSES.Theseresultsareconsisentwiththefindings

presentedintheendnoteforChapter4,demonstratingthatthemechanismsthatoperate

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throughneighbourhoodlowSEStoaffectfetalgrowtharemorestronglyexperiencedwithin

urbanareaswheretherelativedisparitiesaremorepronounced[7].

Table1:ORs(95%CI)forSGA‐3andSGA‐10inrelationtoPM2.5,SES,andRuralResidence

SGA‐3‐m1SES*Rural*PM2.5OR(95%CI)

SGA‐10‐m1SES*Rural*PM2.5OR(95%CI)

tLBW‐m1SES*Rural*PM2.5OR(95%CI)

PM2.5 1.10(1.06–1.15) 1.11(1.09–1.14) 1.11(1.06–1.17)

SESi 0.83(0.80–0.86) 0.85(0.83–0.87) 0.84(0.80–0.88)

RuralResidence 1.06(0.92–1.23) 1.10(1.02–1.19) 1.02(0.85–1.22)

AsianImmigrantDensity 1.07(1.04–1.10) 1.12(1.10–1.14) 1.07(1.03–1.12)

Post‐SecondaryEducation 0.92(0.89–0.95) 0.95(0.94–0.97) 0.89(0.85–0.92)

SESi*PM2.5 0.99(0.96–1.02) 1.00(0.98–1.02) 0.98(0.94–1.02)

Rural*SESi 1.29(1.09–1.52) 1.11(1.02–1.21) 1.44(1.16–1.79)

Rural*PM2.5 1.12(1.00–1.26) 1.05(0.99–1.12) 1.22(1.06–1.41)

MultilevelModelOutputs

Intercept 0.011 0.047 0.007

RandominterceptVariance 0.036 0.017 0.022

MedianOR(95%CI) 1.20(1.11–1.38) 1.13(1.09–1.20) 1.15(1.04–1.76)

Logisticrandominterceptmodelsadjustedfor:maternalage,nulliparous,drugoralcoholflag,maternalsmoking;seasonofbirth,FirstNationon‐reservebirth.

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Figure2:TwodifferentperspectivesofthesamemodelPlotAandBshowthepredictedprobabilitieswith95%CIsfromSGA‐3model‐1(SGA‐3‐m1).PlotCandDshowthepredictedprobabilitieswith95%CIsfromSGA‐10model‐1(SGA‐10‐m1).

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Table2:ORs(95%CI)forSGA‐3,SGA‐10,andtLBWinrelationtoPM2.5,SES,andRuralResidence

SGA‐3‐m2

SES*Rural*PM2.5OR(95%CI)

SGA‐10‐m2SES*Rural*PM2.5OR(95%CI)

tLBW‐m2SES*Rural*PM2.5OR(95%CI)

PM2.5quintile Q1 1.00(reference) 1.00(reference) 1.00(reference)

Q2 1.17(1.01–1.35) 1.16(1.07–1.25) 1.07(0.90–1.28)

Q3 1.27(1.11– 1.46) 1.26(1.18– 1.36) 1.22(1.03–1.45)

Q4 1.34(1.17– 1.54) 1.36(1.26– 1.46) 1.27(1.08–1.52)

Q5 1.41(1.22– 1.64) 1.42(1.32– 1.53) 1.50(1.26–1.77)

SESi 0.93(0.83–1.04) 0.92(0.87–0.98) 1.00(0.87–1.15)

RuralResidence 0.94(0.78–1.13) 1.03(0.94–1.14) 0.73(0.57–0.93)

Rural*PM2.5quintile

Rural*Q1 1.00(reference) 1.00(reference) 1.00(reference)

Rural*Q2 1.13(0.82–1.06) 1.02(0.89–1.17) 1.35(0.97–1.89)

Rural*Q3 1.26(0.92–1.04) 1.28(1.07–1.51) 1.57(1.07–2.31)

Rural*Q4 1.03(0.77–0.94) 1.06(0.84–1.32) 1.92(1.22–3.02)

Rural*Q5 1.34(0.98–1.03) 1.12(0.94–1.34) 1.52(0.91–2.56)

SESi*PM2.5quintile

SESi*Q1 1.00(reference) 1.00(reference) 1.00(reference)

SESi*Q2 0.92(0.80–1.06) 0.98(0.91–1.05) 0.92(0.77–1.10)

SESi*Q3 0.91(0.80–1.04) 0.90(0.84–0.97) 0.81(0.69–0.96)

SESi*Q4 0.82(0.72–0.94) 0.89(0.83–0.96) 0.73(0.62–0.86)

SESi*Q5 0.91(0.80–1.03) 0.93(0.87–1.00) 0.88(0.76–1.03)

Rural*SESi 1.21(1.04–1.41) 1.05(0.96–1.14) 1.33(1.08–1.63)

Modeladjustedfor:maternalage,nulliparous,drugoralcohol flag,maternalsmoking,seasonofbirth,DA‐leveleducation,DA‐levelAsianimmigrantdensity,FirstNationon‐

b h

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Figure3:Predictedprobabilitieswith95%CIsfromSGA‐3model‐2(SGA‐3‐m2)andSGA‐10model‐2(SGA‐10‐m2)

TermLowBirthWeight(tLBW) Table1showstheresultsfortLBWfromaninteractionmodelbetweenPM2.5,SES

andurban‐ruralresidence.Thepredictedprobabilitiesderivedfromthesemodelsare

presentedinFigure4usingtwodifferentvariableperspectives.SimilartoSGA,these

modelsshowisthatrisingPM2.5alwaysincreasestheriskoftLBW,andthatthisriskis

modestlytemperedbyrisingSES,butonlyinurbanareas(Figure4A).Interesting,rural

areasshowareversaloftheSESassociationsuchthathigherSESneighbourhoods

demonstratealargerriskoftLBWcomparedtolowSESneighbourhoods.Thistrendwas

partiallyevidentforSGA‐3,butthewideconfidenceintervalsobscuredtherelationship.The

explanationforthisobservationisunclear;however,itcouldberelatedtothesizeofthe

DAsinruralareaswhichencompassageographicareamuchlargerthanatypical

neighbourhood.AsimilarfindingwasobservedbyAugeretal(2009)whichshowedthat

remoteruralareaswereassociatedwithadversebirthoutcomesamonguniversity

educatedmothersonly[8].

TheMORfortLBW(Table1)signifythat,inthemediancase,arandomlyselected

individualmovingfromalowriskDAtoahigherriskDAwillrealizea15%increasedriskof

tLBW,MOR95%CI:1.15(1.04–1.76).Thus,afteradjustingforindividualand

neighbourhood‐levelknownriskfactors,aperson’sresidentialneighbourhoodstillcarriesa

gooddegreeofrelevancetotheirriskoftLBW.

Non‐linearspecificationsofPM2.5werealsotestedconditionalonSESandurban‐rural

residence.ThequatradicexpressionofthecontinuousPM2.5wasnotsignificant,butthe

quiniletransformationdidshowindicationofnon‐linearity(Table2andFigure4B).For

213

urbanareas,therewasclearmodificationofthePM2.5associationbySESontheriskof

tLBW.Forruralareas,therewasaconsistentincreaseinriskoftLBWwithrisingPM2.5

levels,butshowednostatisticallysignificantdistinctionacrossthelevelsofSES.The4th

quintileforPM2.5displaysananomalyinitseffectestimation,likelyduetosmallnumbers

withinthestrtifications.Theseresultsareconsisentwiththefindingspresentedinthe

endnoteforChapter4,demonstratingthatthemechanismsthatoperatethrough

neighbourhoodlowSEStoaffectfetalgrowtharemorestronglyexperiencedwithinurban

areaswheretherelativedisparitiesaremorepronounced[7].

Figure4:TwodifferentperspectivesofthesamemodelPlotAandBshowthepredictedprobabilitieswith95%CIsfromtLBWmodel‐1inTable1(tLBW‐m1).

Intra‐uterineGrowthRestriction(IUGR)Unliketheothermeasuresofadversefetalgrowth(tLBWSGA‐3,SGA‐10),IUGR

showsaprotectiveassociationwithruralresidenceinFigure1.ThiscouldbewhyIUGRdid

notdemonstrateanytrend(statisticallysignificantorotherwise)ofeffectheterogeneity

betweenurbanandruralbirthswithrespecttoSESandPM2.5.Theexplanationofthis

findingisunclear;however,itcouldbeduetoregionaldifferencesinthecompletionofBC

perinatalformsandmaternalcharts.IUGRdiddemonstratetohaveanon‐linearassociation

withPM2.5,andasignificantinteractionbetweenSESandPM2.5.Thisinteractionwasnot

presentwhenPM2.5wasmodeledasalineareffect(Table4andFigure5).

TheMORforIUGR(Table4)signifiesthat,inthemediancase,arandomlyselected

individualmovingfromalowriskDAtoahigherriskDAwillrealizea34%increasedriskof

IUGR,MOR95%CI:1.34(1.26–1.46).ThisisafairlylargeMOR,evenafteradjustingforthe

individualandneighbourhood‐levelriskfactors,andsuggeststhatasubstantialportionof

thebetween‐DAvarianceisleftunexplained.

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Table4:ORsforgestationaldiabetesinrelationtoPM2.5,SES,andRuralResidence

IUGR

SES*PM2.5OR(95%CI)

PM2.5 1.17(1.12– 1.22)

PM2.5*PM2.5 0.93(0.91– 0.96)

SESi 0.90(0.86– 0.93)

RuralResidence 0.75(0.66– 0.86)

ImmigrantDensity 1.07(1.04– 1.10)

HigherEducation 0.96(0.93– 1.00)

SESi*PM2.5 0.95(0.91– 0.99)

Rural*SESi ‐‐

Rural*PM2.5 ‐‐

MultilevelModelOutputs

Intercept 0.013

RandominterceptVariance 0.096

MedianOR(95%CI) 1.34(1.26– 1.46)

Logisticrandomintercept modelsadjustedfor:maternalage,nulliparous,drugoralcoholflag,maternalsmoking;seasonofbirth,FirstNationon‐reservebirth.

Figure5:Predictedprobabilitieswith95%CIsfromSGA‐3model‐2(SGA‐3‐m2)andSGA‐10model‐2(SGA‐10‐m2)

215

PretermBirth(PTB) Pretermbirthisdefinedasabirthwhichreachedaminimumgestationalageof37

completedweeks.Afterexclusions,therewere231,970singleton(liveandstillborn)births

locatedinlocatedin6,338neighbourhoodDAs(min.=1,max.=781,avg.=36).Excluded

observationsincluded:birthswithmissinggestationalage<37weeks(378),missing

cigarettes/day(n=2,501),andmissingPM2.5(n=1,511).PTBwascategorizedbyits

severitybasedongestationalage(mild:34to36weeks,moderate:32to33weeks,early:28

to31weeks,andveryearly:<28weeks).

Figure6showstheresultsfromapartialproportionalodds(pp‐odds)regression

model(similartoanologitregressionbutallowsthecoefficientsfortheparameterstovary

betweenthelevelsofthecategoricaldependentvariable)[9].Thepp‐oddsmodelallowsthe

researchertoseewhichvariableshaveaconstantriskacrossalllevelsofPTBseverityand

whichoneshavevaryingimportance.Thebottomthreesetsofvariableslistedunderthe

differentPTBseveritieshavevaryingrisks.Whilesomevariableschangeverylittle(e.g.

nulliparous),othershavegreatervariation(e.g.gestationalhypertension).

TheriskofPM2.5wasshowntohaveasmallbutsignificantprotectiveassociationfor

PTBrisk.ThisassociationisconstantforallPTBseveritysub‐types.TheassociationforSES

ontheotherhandshowsacleartrendofincreasingprotectiveassocitionswiththemore

severePTBsub‐types.PTBisacomplexbirthoutcomewithmanyunderlyingriskfactors.

FurtherresearchintotheobservedrelationshipbetweenPTBandPM2.5needstobe

exploredforpotentialbias[10,11].

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Figure6:AdjustedORsand95%CIrange(shadedregions)formaternalandneighbourhood‐levelfactorsassocitedwiththeseverityofPTBrisk.

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