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Air pollution, neighbourhood and maternal-level factors modify the effect of smoking on birth weight:… Erickson, Anders C; Ostry, Aleck; Chan, Hing M; Arbour, Laura Jul 16, 2016

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RESEARCH ARTICLE Open AccessAir pollution, neighbourhood andmaternal-level factors modify the effectof smoking on birth weight: a multilevelanalysis in British Columbia, CanadaAnders C. Erickson1,2, Aleck Ostry2, Hing Man Chan3 and Laura Arbour1,4*AbstractBackground: Maternal smoking during pregnancy negatively impacts fetal growth, but the effect is not homogenousacross the population. We sought to determine how the relationship between cigarette use and fetal growth ismodified by the social and physical environment.Methods: Birth records with covariates were obtained from the BC Perinatal Database Registry (N = 232,291). Maternalsmoking status was self-reported as the number of cigarettes smoked per day usually at the first prenatal care visit.Census dissemination areas (DAs) were used as neighbourhood-level units and linked to individual births usingresidential postal codes to assign exposure to particulate air pollution (PM2.5) and neighbourhood-level attributes suchas socioeconomic status (SES), proportion of post-secondary education, immigrant density and living in a rural place.Random coefficient models were used with cigarettes/day modeled with a random slope to estimate its between-DAvariability and test cross-level interactions with the neighbourhood-level variables on continuous birth weight.Results: A significant negative and non-linear association was found between maternal smoking and birth weight.There was significant between-DA intercept variability in birth weight as well as between-DA slope variability ofmaternal smoking on birth weight of which 68 and 30 % respectively was explained with the inclusion of DA-levelvariables and their cross-level interactions. High DA-level SES had a strong positive association with birth weight butthe effect was moderated with increased cigarettes/day. Conversely, heavy smokers showed the largest increases inbirth weight with rising neighbourhood education levels. Increased levels of PM2.5 and immigrant density werenegatively associated with birth weight, but showed positive interactions with increased levels of smoking. Oldermaternal age and suspected drug or alcohol use both had negative interactions with increased levels of maternalsmoking.Conclusion: Maternal smoking had a negative and non-linear dose-response association with birth weight which washighly variable between neighbourhoods and evidence of effect modification with neighbourhood-level factors. Theseresults suggest that focusing exclusively on individual behaviours may have limited success in improving outcomeswithout addressing the contextual influences at the neighbourhood-level. Further studies are needed to corroborateour findings and to understand how neighbourhood-level attributes interact with smoking to affect birth outcomes.Keywords: Maternal smoking, Multilevel models, Socioeconomic factors, Air pollution, Birth weight, Effect modification* Correspondence: larbour@uvic.ca1Division of Medical Sciences, University of Victoria, Medical Science Bld.Rm-104, University of Victoria, PO Box 1700 STN CSC, Victoria V8W 2Y2, BC,Canada4Department of Medical Genetics, University of British Columbia, Vancouver,BC, CanadaFull list of author information is available at the end of the article© 2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link tothe Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Erickson et al. BMC Public Health  (2016) 16:585 DOI 10.1186/s12889-016-3273-9BackgroundSmoking during pregnancy is a modifiable risk factor as-sociated with adverse birth outcomes and may impartlong-term health consequences [1–3]. This relationshiphowever is confounded by the presence of many otherrisk factors, including maternal age, education, alcoholor drug use [4–6]. Furthermore, it’s been shown thatthese individual-level risk factors have a dose-responseassociation with the level of smoking, with a distinctionbetween heavy smokers (greater than 10 cigarettes perday) and moderate or light smokers [4]. For example,while the prevalence of smoking during pregnancy de-creases with increasing maternal age, the level of smok-ing is heavier among the older mothers who do smoke.As a result, the effect of smoking on birth weight hasbeen shown to be modified by maternal age or othercorrelated risk factors [7, 8]. Similarly, neighbourhood-level factors might directly or indirectly modify the effectof smoking on birth weight such as neighbourhooddeprivation or levels of particulate air pollution [9–11].Exposure to the fine fraction of particulate matter(PM2.5, particles with aerodynamic diameter ≤ 2.5 μm)has shown to be a consistent risk factor associated withreduced birth weight [12]. The complex mixture ofPM2.5 includes elemental and organic carbon com-pounds, metals and gases that stem predominantly fromvehicle exhaust, residential heating and industrial emis-sions [13]. The mechanisms by which PM2.5 and its con-stituents adversely affect the reproductive system are notfully understood; however, evidence supports the poten-tial for a shared mode of developmental toxicity with to-bacco smoke exposure [14–16]. With similar chemicalcomponents, both PM2.5 and tobacco smoke penetratedeep into pulmonary alveolar tissues and translocate toextrapulmonary tissues causing systemic cardiovascularand immunological alterations, including platelet activa-tion, coagulation, endothelial dysfunction, DNA damageand mutagenesis [13, 16, 17].Low SES remains one of the most robust predictors ofadverse pregnancy outcomes such as fetal growth re-striction despite universal health care programs inCanada and Europe [9, 18, 19]. The society-level deter-minants such as poverty, poor education, income in-equality and social discrimination and marginalizationact indirectly on the placenta and fetus through the pro-motion of ‘downstream’ or mediating exposures, stressesand behaviours [20, 21]. In studies of cardiovascular dis-ease, neighbourhood-level factors were associated withincreased levels of smoking and other risk factors suchas obesity, lack of exercise, lower health knowledge andlower positive behaviour changes [11, 22]. These epi-demiological observations have been shown with the useof multilevel statistical models capable of separating theindividual-level effects from the context of their socialand physical environments [23]. The use of multilevelmodels in perinatal epidemiology has uncoveredneighbourhood-level factors that interact with maternal-level risk factors to either buffer or mediate adverse birthoutcomes [21, 24].We present a multilevel cross-sectional analysis ofbirth registry data in British Columbia, Canada (popula-tion 4.6 million) to investigate neighbourhood-level dif-ferences in the effect of cigarette smoking duringpregnancy on birth weight and to quantify the degree towhich individual and neighbourhood-level variables ex-plain any observed differences. Specifically, we sought todetermine whether exposure to PM2.5 and living in lowSES neighbourhoods explain between-neighbourhooddifferences in the effect of maternal smoking on birthweight. We also examine whether these neighbourhood-level factors modify the direct effect of maternal smok-ing on birth weight. Birth weight is among the mostimportant factors affecting neonatal mortality and is asignificant determinant of post-neonatal infant mortalityand childhood morbidity [25]. Understanding the under-lying individual and interactive effects of exposures onbirth weight is crucial for effective community planningand strategic interventions to improving reproductivehealth outcomes.MethodsThis was a population-based cross-sectional study ofsingleton births in British Columbia from 2001 to 2006(N = 237,470). Data from the BC Perinatal DatabaseRegistry were provided by Perinatal Services BC (PSBC),and included information on individual-level maternal-infant health status and outcomes, reproductive history,socio-demographics, risk factors, and residential postalcodes. The Registry accounts for nearly 100 % of birthsand stillbirths in BC of at least 20 weeks gestation or atleast 500 g birth weight. Research data access is providedby a Partnership Accord /Memorandum of Agreementbetween all BC Health Authorities and PSBC throughthe Freedom of Information and Privacy Protection Act[26]. Research ethics board approval was granted by theUniversity of Victoria (protocol #11-043).The outcome variable was continuous birth weight ofsingleton births. Included were all births (stillbirth andlive) for gestational ages of 20 to 42 weeks. Excludedbirth records included: out-of-province and invalid pos-tal codes (n = 1096), non-viable births prior to 20 weeksgestation or less than 500 g (n = 15), and the list-wise de-letion of births missing important data including: ciga-rettes smoked per day (cigarettes/day, n = 2510), PM2.5(n = 1512), birth weight (n = 46). Table 1 provides the fulllist of covariates used along with their summary statis-tics. All continuous independent variables, except ciga-rettes/day, were grand-mean centred and standardizedErickson et al. BMC Public Health  (2016) 16:585 Page 2 of 12to ease interpretation and aid model convergence. Thus,a value of zero represents the transformed variable’smean and reference value and has a standard deviationequal to one. The variable cigarettes/day was kept un-transformed since the value zero (i.e. non-smokers) wasthe desired reference level. Smoking levels were cappedat 20 cigarettes/day with higher values assigned a valueof 20 to stabilize the distribution tail (n = 245, min.21max.80). Two variables indicating the use of alcohol ordrugs (prescription, non-prescription, illicit) to be a riskfactor in pregnancy as identified by a physician werecombined into a single dichotomous variable.Birth records were geocoded based on the latitude-longitude coordinate of the mother’s residential postalcode at the time of delivery using GeoRef by DMTI [27].Birth records were then linked to their correspondingcensus dissemination area (DA) by performing a point-in-polygon spatial join procedure in ArcGIS 10.2 [28].DAs represent the smallest geographical unit for whichcensus data are available with a spatial coverage rangingbetween 200 and 800 people depending on the level ofurban development. While DAs do not necessarily rep-resent existing neighbourhood communities [29], theycan act as proxies for a general catchment area of per-sonal home-life activities [21, 30]. Birth records wereidentified as being either rural or urban using the Statis-tics Canada Metropolitan Influence Zone (MIZ) codeswhich are based on commuting flows of small townsinto larger cities and metropolitan areas [31].PM2.5 exposure was estimated using a national land-use regression (LUR) model developed to estimate PM2.5at the census street block-face level [32]. The modelused a number of predictors including satellite measures,proximity to major roads and industry to account for46 % of the variability in measured annual PM2.5 con-centrations. Individual birth records were related to theblock-face point estimates using a nearest-point proced-ure in ArcGIS10.2. Street block-face point estimates wererelated to individual birth records using a nearest-pointprocedure in ArcGIS10.2 and then aggregated to theirDA-level mean to represent an area-level air pollutionvariable on individual births.Three related but independent datasets all based onthe 2006 Statistics Canada national census were used torepresent the DA-level SES and demographic data. Thefirst was a Canadian SES index (SESi) developed byChan et al. which provides a measure of overall socio-economic neighbourhood well-being [33]. The secondwas the proportion of population over 15 with any post-secondary education, including college, trades, or univer-sity representing higher DA-level education attainmentlevels. The third was the proportion of continental Asianimmigrants by DA. It’s been shown in BC and elsewherethat healthy babies from Asian and South Asian back-grounds are constitutionally smaller compared toCaucasian babies [34, 35]. Asian and South Asian eth-nicities are well-represented throughout BC but par-ticularly in concentrated pockets throughout themajor urban center of Metro Vancouver where levelsof PM2.5 are also high and could therefore confoundany PM2.5 effect. Furthermore, concentrated ethniccommunities may impart buffering mechanismsthrough enhanced social interactions and support net-works [21, 24]. A sequential regression technique wasused to remove the collinearity between sets of DA-level variables [36]. Here, immigrant density wasregressed against SESi and PM2.5 with the saved residualsrepresenting the uncorrelated and independent contribu-tion of immigrant density on birth weight freed from itscollinearity with SESi and PM2.5 (r = −0.62 and 0.53 re-spectively). This method was repeated for SESi and educa-tion (r = 0.26) creating a residual immigrant density andresidual education variable. The education and immigrantdata were obtained by access to ABACUS via the DataLiberation Initiative [37].Imputation for missing SES, education and immigrantdensity values was performed in order to avoid data lossof rural DAs with low population counts. Taking advan-tage of the nested hierarchical structure of the adminis-trative census and health boundaries, the mean SESivalue for a larger encompassing census subdivisionTable 1 Descriptive statistics# for individual (Level-1) and DA(Level-2) covariates on term birth weightVariable Mean (sd) Min-maxLevel-1 (individual)Maternal age 29.8 (5.60) 11 – 55Nulliparous 0.45 (0.50) 0 – 1Drug/Alcohol flag 0.02 (0.15) 0 – 1Cigarettes/day 0.79 (2.91) 0 – 20Fall/Winter season 0.48 (0.50) 0 – 1Level-2 (DA) VariablesSESi -0.08 (0.58) -2.22 – 1.18Education 0.50 (0.12) 0 – 0.95Immigrant density 0.16 (0.19) 0 – 0.86PM2.5 7.30 (0.86) 4.41 – 10.23Rural address 0.11 (0.32) 0 – 1# values shown are unstandardized, non-centered; Nulliparous: patient hasnever delivered a baby of at least 500 g birth weight or at least 20 weeksgestation in a previous pregnancy; Drug or Alcohol Flag: physician indicateduse of drugs (prescription, non-prescription, illicit) or alcohol as risk factor inpregnancy; Cigarettes/day: number of cigarettes smoked daily at 1st prenatalvisit (self-reported); Fall/Winter Season: month or birth between September toFebruary; SESi: socioeconomic status index; Education: proportion of populationover 15 with any post-secondary education (trade, college, university); ImmigrantDensity: proportion of the population identified as immigrant status fromcontinental Asia; PM2.5: Particulate Matter less than 2.5 μm; Rural: thosehaving a rural residential addressErickson et al. BMC Public Health  (2016) 16:585 Page 3 of 12(CSD) or local health area (LHA) was imputed for anested DA with a missing value. There were 1441 valuesimputed in 52 DAs for SESi (0.6 % of final N, 0.8 % ofDAs), and 3170 values imputed in 108 DAs for botheducation and immigrant density (1.4 % of final N, 1.7 %of DAs). Sensitivity analyses were performed using onlythe non-imputed data.Hierarchical (multilevel) linear regression models wereused to test our research questions, thereby accountingfor the clustering, or non-independence, of individuals(level-1) belonging to a given DA neighbourhood (level-2). The multilevel model allows the intercept and slopeto act as random parameters having between-area (DA)variability from an overall (BC-wide) mean intercept andslope. Therefore each DA has its own intercept andslope in which their variability from the overall meanintercept and slope can be investigated with the additionof individual (level-1) and DA-level (level-2) variablesand their interactions [38]. We followed a bottom-up ap-proach to model building to quantify the explained pro-portional change in variance (PCV) with the addition ofsets of variables, the multilevel model equivalent to anR2 [23]. We started with the empty (Null) random inter-cept model without any independent variables in whichbirth weight is only a function of the mother’s residentialDA. The presence of significant random intercept vari-ance indicates there are unexplained differences betweenneighbourhood means of birth weight. The proportionof the total variance in birth weight that arises due toneighbourhood differences can be quantified by comput-ing the intra-class correlation (ICC) which representsthe degree of clustering of individual birth weight withinneighbourhoods [23].The Null model was followed by Model that includedthe individual-level covariates as well as the addition of arandom slope for the continuous variable of maternalsmoking (cigarettes/day, self-reported at the first prenatalvisit). By allowing cigarettes/day to be random, the meanwithin-DA effect of maternal smoking is allowed to differbetween DAs. The presence of a significant random slopeindicates that its effect on birth weight is not constant (orequal) for all DAs; that is, there are important unexplaineddifferences between the within-DA group effects of mater-nal smoking on birth weight. Subsequent models includedthe DA-level variables along with cross-level interactionsto assess their fixed effects on birth weight but to also de-termine if their inclusion addresses any unexplained slopevariance. Several models were tested using the Akaike In-formation Criterion (AIC) to evaluate model performance.We report the results of three models to compare thedegree of change between the level-1 and level-2 homoge-neous (non-interaction) models and a model with effect-measure variation. All statistical analyses were conductedin Stata 13IC [39].ResultsAfter exclusions, the final dataset included 232,291 single-ton (live and stillborn) births located in 6338 neighbour-hood DAs (min. = 1, max. = 782, avg. = 37). Table 1summarizes the untransformed individual and neighbour-hood covariates (non-centered, non-standardized). Theprevalence of maternal smoking in this population was10.3 % (n = 23,836) with an average of 7.5 cigarettes/dayamong smokers. Table 2 reports the adjusted coefficientsfor the individual and DA-level covariate fixed effects oncontinuous birth weight (Model 1 and 2). Model 1 was alevel-1 model that included only the maternal-level covari-ates. The relationship between birth weight and ciga-rettes/day was found to be non-linear and was bestmodeled using a quadratic term indicating a subdueddose-response with increasing exposure (Fig. 1). Model 2added the DA-level variables. Their fixed effects show thatDAs with higher SES and higher proportion of post-secondary education were significantly associated withhigher birth weights; whereas DAs with increased levels ofPM2.5, higher Asian immigrant density and rural DAswere all significantly associated with lower birth weights.Season of birth (fall or winter) was also significantly asso-ciation with reduced birth weight. The results in Table 2represent the fixed effects from homogeneous models (i.e.those without any modeled heterogeneity of the effectmeasure for maternal smoking).Model 3 tested interactions with cigarettes/day includ-ing cross-level (level-1 by level2) and level-1 by level-1interactions to explain the between-DA random inter-cept and random slope variability. The model results arepresented in Table 3 including the main effects as wellas the interaction effects with cigarettes/day. The degreeTable 2 Adjusted fixed effects for level-1 and level-2 covariateson continuous term birth weightVariables Model 1β (95 % CI)Model 2β (95 % CI)Maternal age -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/Alcohol flag -171.6 (-186.9 – -156.3) -172.2 (-187.5 – -157.0)Cigarettes/day -23.5 (-25.8 – -21.2) -26.2 (-28.5 – -23.9)cigarettes/daya 0.66 (0.51 – 0.80) 0.75 (0.61 – 0.90)Fall/Winter season -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)Immigrant density – -35.8 (-38.5 – -33.2)Rural address – -18.8 (-28.4 – -9.2)PM2.5 – -25.0 (-28.2 – -21.8)PM2.5a – 3.3 (1.5 – 5.2)See Table 1 caption for variable definitionsaModeled as a quadraticErickson et al. BMC Public Health  (2016) 16:585 Page 4 of 12of heterogeneity across levels of maternal smokingmodified by the DA-level contextual factors is graphic-ally presented in Fig. 2. The five graphs show the pre-dicted conditional fixed effects of SESi, education, PM2.5,Asian immigrant density and rural residence on birthweight and their interactions with specified levels of ma-ternal smoking (Fig. 2a–e respectively). For example,Table 3 and Fig. 2a show that higher SES has a signifi-cant positive association with birth weight but is lesspronounced with increased levels of maternal smokingwhereby very heavy smokers (≥20 cigarettes/day) do notincur any benefit of higher SES. Conversely, very heavysmokers showed the greatest gains in birth weight inDAs with higher proportions of post-secondary educatedpeople (Fig. 2b). Recall that the higher education variablewas an uncorrelated residual variable independent ofSESi, and therefore these observed associations are inaddition to the education-related effect captured bySESi.Increasing PM2.5 levels had a significant non-linearassociation with reduced birth weight; however, itshowed a positive interaction with maternal smokingsuch that the effect of increased smoking on birthweight was attenuated in DAs with higher levels ofPM2.5 (Fig. 2c). Similarly, higher Asian immigrantdensity was significantly associated with lower birthweights but had a positive interaction with increasedcigarette use demonstrating a protective effect ofFig. 1 Adjusted Predicted Effects of Maternal Smoking on Birth Weight. Predicted effects of maternal smoking (cigarettes/day) on birth weightwith 95 % confidence intervals are conditional on model covariates included in Model 3. Black vertical lines represent the frequency distributionof cigarettes/day (non-smokers, 0 cigarettes/day, have been omitted for display purposes)Table 3 Adjusted individual and DA-level fixed effects on continuous birth weight and their modification by maternal smoking(Model 3)Variables Main effectβ (95 % CI)Modification by cigs/dayβ (95 % CI)Corresponding figureCigarettes/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) 2AEducation 5.2 (2.3 – 8.1 1.3 (0.3 – 2.3) 2BPM2.5 -26.3 (-29.6 – -23.0) 1.8 (0.9 – 2.7) 2CPM2.5a 3.4 (1.5 – 5.3) – –Immigrant density -36.5 (-39.2 – -33.7) 2.6 (1.5 – 3.7) 2DRural address -15.0 (-25.1 – -5.0) -2.9 (-5.6 – -0.2) 2EMaternal age -12.1 (-14.8 – -9.5) -2.9 (-3.6 – -2.1) 3ADrug/Alcohol flag -161.2 (-180.4 – -142.1) -3.7 (-6.3 – -1.2) 3BSee Table 1 caption for variable definitionsaModeled as a quadratic, value for Cigarettes/day listed under ‘Modification by cigs/day’; Model 3 covariates not listed above include: nulliparous and seasonof birthErickson et al. BMC Public Health  (2016) 16:585 Page 5 of 12higher immigrant density DAs (Fig. 2d). Rural DAshad a significant negative interaction with maternalsmoking indicating a further reduction in birth weightwith increased cigarette use among rural residents(Fig. 2e).Two level-1 interactions with maternal smoking weresignificant, maternal age and suspected drug or alcoholuse. The predicted conditional marginal effects of thesetwo interactions are show in Fig. 3a and 3b respectivelyindicating that the reduction of birth weight amongFig. 2 Adjusted Predicted Effects of Maternal Smoking on Birth Weight across DA-level Factors. a Socioeconomic Status Index (SESi) b Proportionof Population with Post-secondary Education c Particulate Matter≤ 2.5 μm (PM2.5) d Asian Immigrant Density e Rural Residence. Predicted effectson birth weight with 95 % confidence intervals are conditional on model covariates included in Model 3. Black vertical lines represent the frequencydistribution of the variable on the x-axis (except Fig. 2e which shows the frequency distribution of cigarettes/day)Erickson et al. BMC Public Health  (2016) 16:585 Page 6 of 12heavier smokers is exasperated by older maternal ageand those suspected of drug or alcohol use. A variablefor neighbourhood-level smoking (DA-average ciga-rettes/day) was created and tested in models along witha cross-level interaction with maternal-level cigarettes/day but neither parameters were significant nor ex-plained any additional variability.The random effects, the explained proportional changein variance (PVC), and model diagnostics are presentedin Table 4. The unadjusted ICC for the Null random-intercept model was 0.019, indicating that 1.9 % of thetotal residual differences in birth weight are attributableto DA-level contextual factors. However, the ICC in-creased to 2.2 % for Model 1 with the inclusion of thelevel-1 covariates and random slope for cigarettes/day.This was due to the reduction in the level-1 residualvariance (560.5 to 554.7) relative to the increase in thelevel-2 random intercept variance (78.7 to 83.4). The(now adjusted) ICCadj is conditional for the individualcomposition of the DAs including the random slope forcigarettes/day held constant at 0 (i.e. non-smokers). Theaddition of DA-level variables in Model 2 removed a lotof the DA-level variance reducing the ICCadj to 0.6 %.The level-2 random intercept variance term (reportedas standard deviations in Table 4) indicates that themean birth weight for every DA has a degree of variabil-ity from the overall (BC-wide) mean birth weight. Forthe Null model, the overall birth weight intercept is3434.3 g with a standard deviation of 78.7 giving an 8.6% difference in range between 95 % of the DAs (3434.3± (1.96 × 78.7) = 3280.0 and 3588.6 g). The quadraticform of the random slope for cigarettes/day in Model 1prevents a similar calculation to be performed, but Fig. 4gives an indication of the large between-DA slopeFig. 3 Adjusted Predicted Effects of Maternal Smoking on Birth Weight across Maternal-level Factors. a Maternal age b Suspected Drug or AlcoholUse. Predicted effects on birth weight with 95 % confidence intervals are conditional on model covariates included in Model 3. Black vertical linesrepresent the frequency distribution of the variable on the x-axis (except Fig. 3b which shows the frequency distribution of cigarettes/day)Erickson et al. BMC Public Health  (2016) 16:585 Page 7 of 12variability which shows the DA-specific slopes of mater-nal smoking on birth weight. The intercept-slope correl-ation shown in Table 4 indicates the presence of DA-level heterogeneity signifying that DAs with higher aver-age birth weights from non-smoking mothers have alower within-DA effect of smoking (i.e. higher averageDA intercepts tend to have lower average slopes forsmoking) [23, 38].The level-1 and level-2 explained PCV (L1-PCV &L2-PCV) summarizes the relative degree of explainedvariance at the different levels between the differentmodels (Table 4). Using the Null model as the refer-ence, the Model 1 resulted in an L1-PCV of 2.0 %, andthe L2-PCV in the random intercept was−12.3 %. Thenegative L2-PVC is a result of the larger level-2 inter-cept variance relative to the Null model. The additionof the DA-level variables in Model 2 explained 68.2 %of the DA-level variance compared to the Null model.Model 3 accounted for an additional 0.3 % of the L2-PCV.Sensitivity analyses using only the non-imputed DAs(N1 = 229,067 in 6230 DAs) showed very minor differ-ences in magnitude of significant variables. Most of theDAs that were missing data were in rural areas withsmall population numbers, the likely reason why theirdata were suppressed from the census tables. Whilesome parameters were slightly attenuated, many of theinteraction terms increased in magnitude. The random-intercept standard deviation was also slightly smallerthan that of the same model using the full dataset whilerandom-slope standard deviation showed no difference.In a second sensitivity analysis, we restricted the sampleto only term births excluding stillbirths and congenitalanomalies. As expected, there was a large reduction inthe random slope variability (L2 slope (sd) = 5.7) and asmall decrease in the random intercept variability (L2intercept (sd) = 43.0) due to using only term births.Changes in the coefficients for the DA-level variables aswell as their cross-level interactions with cigarettes/daywere minor, within their 95 % confidence intervals re-ported in Table 3. The exception was the main effect ofeducation which was no longer significant (p = 0.151),but its interaction with cigarettes/day did remain signifi-cant (p = 0.025). The maternal-level variables were atten-uated but remained significant with the exception of theinteraction between drug/alcohol flag and cigarettes/daywhich was no longer significant (p = 0.106).Table 4 Random Effects and Model DiagnosticsNullmodelModel 1 Model 2 Model 3L1 residual (sd) 560.5 554.7 555.0 554.9L2 intercept (sd) 78.7 83.4 44.4 44.2L2 slope (sd) – 10.7 9.8 9.0Intercept 3434.3 3505.9 3501.8 3500.9AIC 602672 598513 596639 596514L1-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 0.006Int-slope corr. – -0.53 -0.28 -0.28Abbreviations: L1 residual (sd) Level-1 residual standard deviation, L2 intercept(sd) Level-2 random intercept standard deviation, L2 slope (sd) Level-2 randomslope standard deviation, PCV proportional change in variance, #VPC (variancepartition coefficient) is equivalent to the ICC but conditional on the random-slope variable, thus values in table represent intercepts for non-smokingindividuals, Int-slope corr intercept-slope correlationFig. 4 Neighbourhood-specific slopes of maternal smoking on birth weight. Empirical Bayes predictions of DA-specific regression lines for Model 1Erickson et al. BMC Public Health  (2016) 16:585 Page 8 of 12DiscussionThis study employed multilevel random coefficientmodels to assess whether neighbourhood-level context-ual factors can modify the effect of maternal smoking onbirth weight. Our results show that the effect of mater-nal smoking on birth weight, self-reported as the num-ber of cigarettes smoked per day, is modified by bothindividual-level and neighbourhood-level variables.However, the observed direction of the effect modifica-tion was not always as expected. Furthermore, throughthe use of random-slope models we show that the aver-age effect of maternal smoking on birth weight can varyconsiderably between neighbourhoods which was onlypartially explained by the cross-level interactions. Afteradjusting for individual-level covariates and DA-levelsocio-economic, socio-demographic and air quality vari-ables, there was a significant non-linear effect betweencigarettes/day and birth weight in BC for singletonbirths from 2001 to 2006. This association was robust tothe exclusion of stillbirths and congenital anomalies aswell as the use of only term births demonstrating thatselection bias does not likely affect the observed results.The observed non-linear association between ciga-rettes/day and birth weight shown in Fig. 1 suggests thatthe largest potential effects are seen at the low to middlerange of smoking levels. England et al. [40] found a verysimilar non-linear association of maternal smoking onterm birth weight using self-reported cigarettes/day aswell as using urine cotinine concentrations. Therefore,efforts to reduce the number of cigarettes smoked dur-ing pregnancy may have limited results for moderateand heavy smokers without substantial reductions or fullcessation [41]. Interestingly, we found a similar curvilin-ear relationship with increasing levels of modeled PM2.5and birth weight (Fig. 2c), a dose-response phenomenonobserved in other exposure-disease contexts [42].Beyond the non-linear association between ciga-rettes/day and birth weight, other factors were able tomodify this relationship both positively and negatively.Our analysis confirm previously shown modification ofthe smoking-birth weight relationship by maternal riskfactors [7, 8]; however, to our knowledge this is the firststudy to show that neighbourhood-level factors are ableto modify this relationship. We found a significantnegative interaction between cigarettes/day andneighbourhood-level SESi that resulted in the attenu-ation of the beneficial role of rising neighbourhood-level SES on birth weight with increased levels ofmaternal smoking (Fig. 2a). The predicted effects pre-sented in Fig. 2a suggests is that maternal smoking mayhave little relevance in affecting birth weight in verylow SES neighbourhoods, but becomes more prominentas neighbourhood-level SES increases and perhapsother stressors negatively impacting birth weight arereduced. Hence interventions focusing exclusively on indi-vidual behaviours may have limited success without ad-dressing the contextual influences at the neighbourhood-level [9, 43–45].Conversely, the small but significant positive inter-action between higher proportions of neighbourhood-level post-secondary education and cigarettes/dayfound that heavy smokers may benefit the most by liv-ing in higher educated neighbourhoods (Fig. 2b). Thistype of cross-level effect has been observed in otherepidemiological scenarios where higher risk individualshave better outcomes than would be expected due tosome beneficial capacity of the neighbourhood context[11, 22]. The mechanisms by which neighbourhood-level factors affect individual health is indirectly exertedthrough individual-level processes, such as behaviours,adaptations and attitudes which may be transmitted be-tween people [46, 47]. Meng et al. found that low edu-cation neighbourhoods exert an impact on low birthweight and preterm birth through unhealthy behav-iours, psycho-social stress (i.e. sense of control) andSES-related support [21]. Therefore it could be thatsmoking cessation rates in pregnancy are higher in bet-ter educated neighbourhoods where healthier behav-iours are more common [48, 49]. Fig. 2b suggests thatliving in higher educated neighbourhoods may encour-age moderate and heavy smokers to reduce their smok-ing frequency to less than five cigarettes/day.Neighbourhood social supports and transmission ofbehaviours could also explain the observed interactionswith higher immigrant density and rural address, albeitin opposite directions. The positive interaction betweenhigher immigrant density and maternal smoking (Fig. 2d)may reflect the buffering effect of strong community co-hesiveness and beneficial cultural practices [21, 43, 47].Conversely, the observed negative interaction betweenrural address and cigarettes/day (Fig. 2e) could be due tothe transmission of negative behaviours due to such be-haviours being more common [50], and where less sup-port for cessation may lead to smoking throughoutpregnancy [51]. The dichotomized definition used torepresent rural residential addresses may obscure mech-anisms which can be modified by maternal factors suchas education [52].The buffering effect of PM2.5 with increased levels ofmaternal smoking (Fig. 2c) is curious but could provideevidence for a protective pre-conditioning stress that ac-tivates an adaptive response and increases biological re-sistance to cigarette-induced harms [53, 54]. We found asimilar positive interaction between suspected alcoholand drug use and PM2.5 in a different analysis [55]. Thesuspicion of survival bias due to competing risks waspartly mitigated by using a near full population samplethat included stillbirths, congenital anomalies andErickson et al. BMC Public Health  (2016) 16:585 Page 9 of 12preterm births, although we were not able to control forfetal loss prior to 20 weeks gestation. Other explanationsrequire further scrutiny as evidence of the opposite(negative and synergistic) effect between smoking andair pollutants has been shown [10, 16].We have shown in an earlier paper that women whoreported smoking 10 or more cigarettes/day at their firstprenatal visit were significantly more likely to have othermaternal risk factors, such as lower education, suspecteddrug or alcohol use, and fewer prenatal care visits [4].Our current results compliment the previous study byshowing that the cumulative impact of multiple risk fac-tors can have more than an additive effect on birthweight reduction. The negative association betweenolder maternal age and birth weight was markedlygreater with increased levels of maternal smoking, par-ticularly among the heaviest smoking group (Fig. 3a).Similarly, those who reported higher levels of smokingwho were also suspected of drug or alcohol use showeda pronounced effect compared to those who reported tonot smoke (Fig. 3b). These results corroborate the estab-lished literature showing similar synergistic interactionsbetween both maternal alcohol use and smoking onlower birth weights [8, 56], as well as between maternalsmoking and older maternal age on birth weight [7, 57].While the application of multilevel models in perinatalepidemiology have become more common [58], mosthave been random-intercept models with very few in-cluding a random-slope parameter. Permitting the slopefor the maternal cigarettes/day exposure to be randomprovides information on how its effect on birth weightdiffers between neighbourhoods and enables the searchfor neighbourhood-level variables to help explain thebetween-neighbourhood variance [38]. For example, therandom-slope standard deviation presented in Table 4drops from 10.7 in Model 1 to 9.0 in the fully adjustedModel 3. This represents a 30 % change in explainedrandom-slope variance (10.72–9.02/10.72). Furthermore,the addition of the level-2 variables explained 68.5 % ofthe random-intercept variance compared to the Null(empty) model. However in light of these findings, sig-nificant inter-DA variance remained for both the ran-dom intercept and slope.This study used self-reports of smoking (cigarettes/day) recorded at the first prenatal visit; however, therewere no data on exposure to environmental tobaccosmoke or whether smoking reduction or cessation oc-curred during the pregnancy. The self-reporting biasof cigarette consumption can lead to the attenuationof the true effect of smoking on birth weight [59], andmay therefore alter observed interactions. Studies ofsmoking misclassification in the United States has es-timated non-disclosure to be around 20 % [60, 61].The demographic predictors of non-disclosure includeformer smokers and younger maternal age whichcould partially explain the observed interaction be-tween maternal age and cigarettes/day [61]. Similarly,recall bias and perceived stigma may result in under-reporting of actual consumption habits. This could ac-count for the observed curvilinear effect on birthweight if women smoking 10 cigarettes/day reportonly smoking 5 per day, although England et al. ob-served a similar slope using urine-cotinine concentra-tions [40]. While relatively small, sensitivity analysesregarding the list-wise deletion of observations withmissing smoking data was to exclude potentially at-risk pregnancies and could therefore alter coefficientestimates (n = 2501, 1.1 % of sample).Another limitation includes potential measurementerror and misclassification bias in the PM2.5 exposureassessment which could affect its estimates. First, theLUR PM2.5 concentrations may be underestimatedwith less variability compared to compiled monitoringdata which could potentially underestimate its associ-ation with birth weight in certain areas [62]. Also thePM2.5 LUR model is cross-sectional based on 2006 airquality monitoring data, and we therefore assume thatthe study population was exposed to the same levelsof PM2.5 across 6 year study period based on theirresidential DA. Finally, our analysis was based on ma-ternal place of residence at delivery, and thereforeintra-urban commuting and potential inter-urbanrelocation within the pregnancy period was notaccounted for. Time-activity patterns show that preg-nant women spend more time at home in the laterstages of pregnancy, but mobility patterns may differby age, parity and SES [63, 64].A main strength of this study is the quality of theperinatal registry data [65]. The near 100 % ascertain-ment of birth records for the province of BC and qual-ity control measures used in database managementpractices produces highly reliable data on maternaland newborn health outcomes, co-morbidities andexposures. However, the inability to control forindividual-level SES, particularly maternal education,may influence the neighbourhood-level effect esti-mates and interactions. Maternal education is a vari-able provided in the PSBC Perinatal Registry, but wasonly available for 10 % of our population cohort. Theadjustment for socially-patterned behavioural risk fac-tors such as maternal smoking and suspected drug oralcohol use will control for some individual-level SESdifferences [4]. Notwithstanding, our results suggestthat reported number of cigarettes smoked correlateswith a substantial reduction in birth weight and ismodified by socioeconomic, demographic and envir-onmental risk factors suggesting the information asprovided will help identify those at highest risk.Erickson et al. BMC Public Health  (2016) 16:585 Page 10 of 12ConclusionsThe effect of maternal smoking on birth weight is notconstant across geography, but rather is context specificgiven the social and physical environment. The use ofrandom coefficient models revealed neighbourhood-leveldifferences in how maternal smoking negatively im-pacted birth weight demonstrating effect modification byneighbourhood and maternal-level factors. The inclusionof the DA-level SES, demographic and PM2.5 variablesexplained 68.5 % of the random intercept variability inDA-mean birth weight. However, the random slope vari-ability was only partially explained by the cross-levelinteractions suggesting other contextual factors are in-volved in determining the magnitude of maternal smok-ing on birth weight. Further studies are needed tocorroborate our findings and to understand howneighbourhood-level attributes interact with smoking toaffect birth outcomes.AcknowledgementsWe would like to acknowledge Dr. Scott Venners for his contributions inobtaining the CIHR Operational grant. We acknowledge the staff at PerinatalServices BC for their support regarding data access and review ofmanuscript. All inferences, opinions, and conclusions drawn in thispublication are those of the authors and do not reflect the opinions orpolicies of Perinatal Services BC.FundingThis publication was funded in part by the Canadian Institute of HealthResearch (CIHR) Operational Grant (protocol #: 200903-202069). Fundersprovided no role in the design, conduct, data collection, management,analysis, interpretation, preparation, review and approval of this study.Availability of data and materialsThe Data Sharing Agreement signed with Perinatal Services BC (PSBC) doesnot allow for the data to be shared; however, permissions can be grantedunder a formal request to and approval from PSBC.Authors’ contributionsAE was primarily responsible for the study design, acquisition of data,analysis and interpretation of results, drafting the article and makingrevisions. LA critically assessed the concept, study design, analysis andinterpretation of results. LA provided critical edits of the content. Finalapproval of submitted manuscript was provided by both authors. LC and AOprovided useful edits on draft manuscripts and input into the manuscriptframework. All authors give final approval of the version to be published.Competing interestsThe authors declare they have no competing financial interests.Ethics approval and consent to participateResearch ethics board approval was granted by the University of Victoria(protocol #11-043).Author details1Division of Medical Sciences, University of Victoria, Medical Science Bld.Rm-104, University of Victoria, PO Box 1700 STN CSC, Victoria V8W 2Y2, BC,Canada. 2Department of Geography, University of Victoria, David Turpin Bldg.Rm-B203, University of Victoria, PO Box 1700 STN CSC, Victoria V8W 2Y2, BC,Canada. 3Center for Advanced Research in Environmental Genomics,University of Ottawa, 20 Marie-Curie, Ottawa K1N 6 N5, ON, Canada.4Department of Medical Genetics, University of British Columbia, Vancouver,BC, Canada.Received: 30 January 2016 Accepted: 24 July 2016References1. U.S. Department of Health and Human Services. Reproductive andDevelopmental Effects. 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J Expo Sci Environ Epidemiol. 2012;22(5):429–38.65. Frosst G, Hutcheon J, Joseph KS, Kinniburgh B, Johnson C, Lee L. Validatingthe British Columbia perinatal data registry: a chart re-abstraction study.BMC Pregnancy Childbirth. 2015;15(1):123.•  We accept pre-submission inquiries •  Our selector tool helps you to find the most relevant journal•  We provide round the clock customer support •  Convenient online submission•  Thorough peer review•  Inclusion in PubMed and all major indexing services •  Maximum visibility for your researchSubmit your manuscript atwww.biomedcentral.com/submitSubmit your next manuscript to BioMed Central and we will help you at every step:Erickson et al. BMC Public Health  (2016) 16:585 Page 12 of 12

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