Relations between neighbourhood socioeconomic status and birth outcomes are mediated by maternal weight

Accepted Manuscript Relations between neighbourhood socioeconomic status and birth outcomes are mediated by maternal weight Zahra M. Clayborne, Gerald F. Giesbrecht, Rhonda C. Bell, Lianne M. TomfohrMadsen PII:

S0277-9536(16)30721-3

DOI:

10.1016/j.socscimed.2016.12.041

Reference:

SSM 10982

To appear in:

Social Science & Medicine

Received Date: 16 August 2016 Revised Date:

24 December 2016

Accepted Date: 28 December 2016

Please cite this article as: Clayborne, Z.M., Giesbrecht, G.F., Bell, R.C., Tomfohr-Madsen, L.M., Relations between neighbourhood socioeconomic status and birth outcomes are mediated by maternal weight, Social Science & Medicine (2017), doi: 10.1016/j.socscimed.2016.12.041. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Relations between Neighbourhood Socioeconomic Status and Birth Outcomes are Mediated by Maternal Weight

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Zahra M. Clayborne, BSc (Hons)a Gerald F. Giesbrecht, PhDa,b,d Rhonda C. Bell, PhDc Lianne M. Tomfohr-Madsen, PhDa,b,d

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Department of Psychology, University of Calgary, Calgary, AB, Canada; b Department of Pediatrics, Alberta Children’s Hospital, Calgary, AB, Canada; c Department of Agricultural, Food and Nutritional Science, University of Alberta, Edmonton, AB, Canada; d Alberta Children’s Hospital Research Institute for Child and Maternal Health (ACHRI), Calgary, AB, Canada

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Corresponding Author1: Zahra Clayborne, Department of Psychology, University of Calgary, EDC 292G, 2500 University Drive NW, Calgary, AB, T2N 1N4. Phone: +1 (403) 220-6024. 1

Present Address: Zahra Clayborne, School of Epidemiology, Public Health and Preventive Medicine, University of Ottawa, Room 3105, 451 Smyth Road, Ottawa, ON, K1H 8M5. Email: [email protected]. Phone: +1 (403) 850-7825

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Abstract Lower neighbourhood-level socioeconomic status (SES) has been repeatedly associated with an

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increased risk of adverse birth outcomes, even after controlling for individual-level SES. Few studies have empirically assessed potential mechanisms underlying the associations. The

objectives of this study were to (1) examine relations between neighbourhood SES and birth outcomes, and (2) explore if maternal weight variables mediated these relations. Data came from

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a provincial prospective pregnancy cohort study in Canada. Census data was used to create a

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continuous measure of neighbourhood SES. Using information from maternal questionnaires and medical records, two mediators (pre-pregnancy body mass index (BMI), and gestational weight gain (GWG)) and five birth outcomes (preterm birth, low birth weight, macrosomia, small for gestational age (SGA), large for gestational age (LGA)) were examined. After adjusting for individual-level covariates, mediation analyses supported significant associations between lower

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neighbourhood SES and increased risk of macrosomia (b = .1183, 95% BCa CI: .0607-.1896) and LGA (b = .0565, 95% BCa CI: .0040-.1186) through higher pre-pregnancy BMI. Significant associations were also observed between neighbourhood SES and macrosomia, LGA, and

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preterm birth (b = .0105, 95% BCa CI: .0014-.0246) through pre-pregnancy BMI and GWG in

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tandem; pairwise comparisons suggested that associations with macrosomia and LGA through pre-pregnancy BMI alone were significant over associations through pre-pregnancy BMI and GWG together. These findings add to a growing body of literature assessing potential mechanisms underlying relations between neighbourhood SES and adverse birth outcomes, and suggest that neighbourhood-level SES may influence birth outcomes through maternal weight.

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Keywords: Body mass index, birth outcomes, gestational weight gain, maternal weight,

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socioeconomic status

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An extensive body of literature demonstrates associations between lower socioeconomic status (SES) and increased risk of poor birth outcomes. Socioeconomic disparities in birth

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outcomes are pervasive and occur at both the individual- and neighbourhood-levels. Several studies have reported associations between lower neighbourhood SES and higher risk of adverse birth outcomes including: preterm birth (Auger et al., 2012; Luo et al., 2004; Pickett et al., 2002), small-for-gestational age (SGA) births (Agyemang et al., 2009; Luo et al., 2006, 2004), restricted

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fetal growth (O’Campo et al., 1997; Pearl et al., 2001), and infant mortality (Luo et al., 2004).

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Although low individual-level SES (i.e., personal education, income) is a risk factor for adverse birth outcomes (Luo et al., 2006), relations between neighbourhood SES and birth outcomes persist after controlling for individual-level SES and ethnicity (Agyemang et al., 2009; Elo et al., 2009; Hauck et al., 2011; Pearl et al., 2001).

Promoting optimal fetal growth in-utero is essential for a variety of developmental

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outcomes. In the first years of life, both catch-up growth and postnatal growth failure are common in preterm, low birth weight, and SGA infants. Up to 40% of children born preterm and/or of very low birth weight remain below the 10th percentile for weight, length, and head

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circumference at 18-22 months of age (Dusick et al., 2003; Källén, 2000). Postnatal growth

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failure is related to deficits in motor and cognitive development in infancy (Arcangeli et al., 2012) and later childhood (Cooke and Foulder-Hughes, 2003), lower self-esteem in adulthood, lower level of lifetime educational attainment and income, and risk of medical disabilities and the receipt of Social Security benefits in later life (Moster et al., 2008; Saigal et al., 2016). Infants born preterm and/or SGA may also exhibit catch-up growth in early development to compensate for reduced intrauterine growth (Hokken-Koelega et al., 1995; Knops et al., 2005). 3

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An emerging body of research demonstrates associations between catch-up growth and obesity in children (Ong et al., 1999) and long-term risk of cardiovascular disease and Type 2 diabetes in

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adulthood (Crowther et al., 1998; Eriksson et al., 1999). Finally, children born macrosomic and/or large-for-gestational age (LGA) are also at risk. Several studies demonstrate associations between macrosomia and LGA and increased risk of obesity in childhood and adulthood (Boney et al., 2005; Eriksson et al., 2001; Rasmussen and Johansson, 1998), as well as obesity-related

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outcomes ranging from metabolic syndrome to Type 2 diabetes (Boney et al., 2005; Hermann et

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al., 2010). Taken together, this demonstrates that suboptimal growth patterns in-utero can lead to negative outcomes throughout the lifespan.

Although neighbourhood- and individual-level pathways underlying the relations between neighbourhood SES and birth outcomes have been proposed (Culhane and Elo, 2005; Farley et al., 2006; Meng et al., 2013; Pearl et al., 2001; Pickett et al., 2002), few studies have

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empirically investigated them. In particular, empirical evidence surrounding the influence of maternal weight factors, including pre-pregnancy body mass index (BMI) and gestational weight gain, as potential mechanisms through which neighbourhoods influence birth outcomes is

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lacking. Living in a neighbourhood with lower SES is associated with being overweight or obese

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pre-pregnancy (Ng et al., 2014; van Lenthe and Mackenbach, 2002); in turn, higher maternal weight has been linked to increased risks of preterm birth (Nohr et al., 2009; Smith et al., 2007), macrosomia (Ng et al., 2014; Nohr et al., 2009) and neonatal mortality (Sebire et al., 2001; Smith et al., 2007). Furthermore, living in a lower SES neighbourhood increases the risk that a woman will exceed recommended guidelines for weight gain in pregnancy (Olson and Strawderman, 2003; Rasmussen and Yaktine, 2009). Independent of pre-pregnancy BMI, excessive gestational 4

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weight gain has been linked to increased risks of preterm birth (Bodnar et al., 2010), macrosomia (Cedergren, 2006; Crane et al., 2009; Haugen et al., 2014), and large for gestational age (LGA)

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births (Bodnar et al., 2010; Cedergren, 2006; Stotland et al., 2006); these risks can increase when coupled with high pre-pregnancy BMI (Bodnar et al., 2010; Crane et al., 2009; Frederick et al., 2008). Our group has previously shown that higher pre-pregnancy BMI is associated with

excessive gestational weight gain; thus pre-pregnancy BMI and gestational weight gain may

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operate together in their effects on birth outcomes (Anonymous, 2012). In all, these findings

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support the plausibility that indices of maternal weight serve as pathways through which neighbourhood SES influences birth outcomes. The Current Study

The purpose of the current study was to examine the associations between neighbourhood SES and adverse birth outcomes through maternal weight factors (pre-pregnancy BMI,

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gestational weight gain). Our hypotheses were that: 1) lower neighbourhood SES would be associated with increased odds of adverse birth outcomes after controlling for individual-level sociodemographic factors that have been associated with birth outcomes, 2) pre-pregnancy BMI

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would be higher in women living in lower SES neighbourhoods, 3) pre-pregnancy BMI would be

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associated with gestational weight gain, and 4) pre-pregnancy BMI, both alone and in conjunction with gestational weight gain, would mediate relations between neighbourhood SES and adverse birth outcomes.

Methods

Study Design and Sample

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Data for this analysis comes from a longitudinal pregnancy cohort study involving pregnant women, their partners, and their children living in a Canadian province (Anonymous,

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2014). The study sampling frame includes English-speaking women of at least 16 years of age and at a gestational age of ≤ 26 weeks at the time of inclusion living within the two assessed metropolitan regions included in the study. For the current study, only pregnancy assessments and birth records were used – detailed information regarding participant recruitment, data

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collection, and additional measures are available elsewhere (Anonymous, 2014). For the current

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report, the sample included women who had singleton births and who could be linked to census dissemination areas (DAs) (N = 2,068). Data to construct the neighbourhood SES variable was drawn from the 2011 Canadian Census (comprising the short-form census and National Household Survey). The Health Research Ethics Boards at the study’s host institutions approved

Measures

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the study and informed consent was obtained prior to data collection.

Neighbourhood SES

Neighbourhood SES was assessed through adaptation of a census-based neighbourhood

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deprivation measure developed to examine urban and small-area variations in population health

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status (Bell and Hayes, 2012). Participant postal codes were matched to census DAs and corresponding socioeconomic information to derive neighbourhood SES scores for each individual. DAs represent the smallest standard geographic area for which census information is reported, comprising roughly 200-700 persons (Statistics Canada, 2015). Scores of neighbourhood SES were calculated via the summation of standardized (z-score) values of seven socioeconomic variables: proportion without high school completion, proportion without 6

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university completion, unemployment rate, proportion of lone-parent families, average income, proportion of home owners, and employment ratio. Increasing z-scores reflect lower

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neighbourhood SES; four z-scores (average income, university completion, employment ratio, home ownership) were multiplied by -1 to maintain directionality amongst socioeconomic variables. Weight Measures

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Assessments of body weight prior to and during pregnancy (BMI and gestational weight

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gain) in the study cohort have been described previously (Anonymous, 2012; Anonymous, 2016). Briefly, pre-pregnancy weight was self-reported at the first study visit (≤ 26 weeks gestation) and height was measured to the nearest 0.1 cm (Charder HM200P Portstad Portable Stadiometer) at this time. Women reported their highest weight in pregnancy during follow up visits (3-months postpartum). Total gestational weight gain was calculated by subtracting pre-

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pregnancy weight from the highest weight in pregnancy. In cases where participants either did not report their highest body weight or reported a weight that was lower than had been measured during the 3rd trimester, total gestational weight gain was calculated using measured weight in

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the 3rd trimester (measured to the nearest 0.01 kg; Healthometer Professional 752KI). Pre-

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pregnancy BMI was calculated by dividing pre-pregnancy weight (kg) by measured height (m) squared. Women were categorized according to their pre-pregnancy BMI using standard Health Canada categories (Government of Canada, 2003) as: underweight (< 18.50 kg/m2), normal (18.50-24.99 kg/m2), overweight (25.00-29.99 kg/m2), or obese (≥ 30.00 kg/m2). Women were also classified as being above, below, or meeting gestational weight gain recommendations. Guideline-concordant gestational weight gain was evaluated by classifying total gestational 7

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weight gain for each participant according to pre-pregnancy BMI categories and comparing this to the upper limit of BMI-specific recommendations for total gestational weight gain (Health

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Canada, 2010; Rasmussen and Yaktine, 2009). A summary of recommended weight gain ranges in pregnancy by pre-pregnancy BMI category can be found in the Appendix.

Sensitivity analyses were undertaken to explore the utility of using self-reported prepregnancy weight to calculate BMI. BMI derived from height and weight measured in 528

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participants recruited during the first trimester was compared with BMI derived for the same

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women using measured height and self-reported pre-pregnancy weight. Almost all (99%) women categorized as obese (BMI ≥ 30) using measured values were placed in the same category when self-reported weights were used to derive BMI. Classification into normal and overweight categories using self-reported versus measured values was accurate in approximately 86% of

(Anonymous, 2016) Birth Outcomes

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women. Detailed information regarding these sensitivity analyses can be found elsewhere

Labour and delivery outcomes were extracted from birth records. Five dichotomous

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outcomes were assessed – preterm birth (<37 weeks), low birth weight (<2500 g), macrosomia (>4000 g), small for gestational age (SGA; below the 10th percentile), and large for gestational

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age (LGA; above the 90th percentile). SGA and LGA were assessed using population-based and sex-specific Canadian references (Kramer et al., 2001), with adequate for gestational age (AGA) coded as the referent in each variable. Covariates

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Individual sociodemographic factors with known associations with birth outcomes were controlled for in all analyses. These included annual household income (Hayward et al., 2012;

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Yang et al., 2008), maternal education (Luo et al., 2006; Savitz et al., 2004), maternal ethnicity (Pickett et al., 2002; Savitz et al., 2004; Spong et al., 2011), maternal age (Kenny et al., 2013; Lisonkova et al., 2010), marital status (Shah et al., 2011), and parity (Lisonkova et al., 2010). Annual household income comprised five categories: ≥ $100,000; $70,000-99,999; $40,000-

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69,999; $20,00-$39,999; and < $20,000. Remaining sociodemographic variables were collapsed

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into dichotomous variables due to small cell counts, as follows: ethnicity (Caucasian and minority categories); maternal education (high school and below, above high school); parity (nulliparous, primi-/multiparous); and marital status (single/separated/divorced, married/common-law). Mediation analyses involving non-gestational age dependent outcomes (low birth weight, macrosomia) were further adjusted for gestational age to account for

Statistical Analyses

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associations between total gestational weight gain and length of gestation.

The initial sample comprised of 2,190 maternal-infant pairs. The final sample comprised

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2,068 maternal-infant pairs. 122 exclusions were made for reasons including: (1) repeat

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participation in the study (i.e., women who had two consecutive, separate births) in which case data from the second birth (n = 38) was excluded; and (2) failure to match maternal data to a census DA in-province (n = 84). Due to missing data, analyses involving pre-pregnancy BMI included 1,826 maternal-infant pairs, and analyses including gestational weight gain included 1,636 maternal-infant pairs. Comparisons between assessed and excluded groups yielded no

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significant differences between groups on sociodemographic variables (maternal education, household income, ethnicity, marital status, maternal age, parity; p’s > .05).

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All analyses were carried out using SPSS version 23 (IBM SPSS, Chicago, IL). Basic associations between neighbourhood SES and individual-level sociodemographic factors are presented using Pearson correlations (for continuous variables) and logistic regression for

binomial/multinomial variables. Associations between neighbourhood SES, maternal weight, and

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birth outcomes were performed using linear regression for continuous variables and logistic

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regression for dichotomous variables, adjusting for covariates in each analysis (Tables 1 and 2). Serial mediation analyses to assess associations between neighbourhood SES and birth outcomes through maternal weight were conducted using the PROCESS macro for SPSS (Hayes, 2013), as described below. Mediation

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Serial mediation evaluates whether the effects of an independent variable (IV) on the dependent variable (DV) are transmitted through two or more intervening mediators in series. Using the PROCESS macro for SPSS (Hayes, 2013), mediation models were tested to examine

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associations between neighbourhood SES and one of five birth outcomes through pre-pregnancy

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BMI and gestational weight gain in series. PROCESS relies on an ordinary least squares or logistic regression-based path analytical framework to estimate direct and indirect effects in mediation models. Use of PROCESS is particularly well-suited for the current study, as the procedure serves as a formal test of the indirect effect and overcomes limitations of other approaches by using bootstrapping and bias correction of the bootstrapping distribution. As outlined in Figure 1, three indirect pathways arise through which neighbourhood SES may 10

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influence birth outcomes. These pathways include effects through pre-pregnancy BMI alone (a1*b1), gestational weight gain alone (a2*b2), and pre-pregnancy BMI and gestational weight

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gain in serial (a1*d21*b2). Bootstrapping (n = 10,000) was used to construct 95% bias-corrected, accelerated (BCa) confidence intervals (CIs) for each effect; significant mediation (p < 0.05) was established if CIs did not cross zero. Estimates of the effect, standard errors, and 95% BCa CIs from all pathways are reported (Table 3). Pairwise contrast comparisons between indirect effects

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were also conducted to assess whether indirect effects were statistically different from one

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another – comparisons were significant if CIs did not cross zero (p < 0.05) (Preacher and Hayes, 2008). The rationale for this serial mediation model was derived from previous studies that have examined both pre-pregnancy BMI and gestational weight gain together in relation to birth outcomes (Bodnar et al., 2010; Cedergren, 2006; Heude et al., 2011; Starling et al., 2015). Results

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Characteristics of the Sample

Table 1 reports the sociodemographic characteristics of the sample, and describes differences in these characteristics as a function of neighbourhood SES. The sociodemographic

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characteristics of age, marital status, minority status, education, and income were all significantly

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associated with neighbourhood SES. In detail, lower neighbourhood SES was associated with lower maternal age, minority status, being single/separated/divorced, lower educational attainment and lower annual household income. Continuous neighbourhood SES scores (based on standardized z-scores) ranged from -1.70 to 3.93 (M = -.002, SD = .610), with higher scores representing lower neighbourhood SES. The sample comprised 1,116 census DAs, with a mean ± SD of 1.85 ± 1.73 women per DA. 11

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In the full sample, 5.3% of infants were classified as being low birth weight and 9.1% were classified as macrosomic. Similarly, 9.7% of infants were classified as SGA and 6.7%

Associations between neighbourhood SES and maternal weight

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as LGA. The overall preterm birth rate was 6.7%.

After adjusting for covariates (annual household income, maternal education, ethnicity, maternal age, parity, marital status), neighbourhood socioeconomic status was significantly

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associated with pre-pregnancy BMI (β = .151, t(1587) = 5.813, p < .001). Logistic regression

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analyses further revealed that lower neighbourhood SES was associated with increased odds of excessive gestational weight gain (OR= 1.336, p = .002, 95% CI 1.111-1.608). There were no associations between neighbourhood SES and inadequate gestational weight gain (OR = .960, p =.736, 95% CI .759-1.215)

There was a significant negative association between pre-pregnancy BMI and total

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gestational weight gain (β = -.178, t(1625) = -7.280, p < .001), indicating that higher prepregnancy BMI was associated with lower total gestational weight gain. However, higher prepregnancy BMI was also associated with an increased risk of gestational weight gain in excess of

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recommendations (OR= 1.095, p < .001, 95% CI 1.070-1.120,), and conversely, with a decreased

.975).

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risk of gestational weight gain below recommendations (OR = .945, p < .001, 95% CI .916-

Associations between neighbourhood SES and birth outcomes After adjusting for covariates (annual household income, maternal education, ethnicity,

maternal age, parity, marital status), logistic regression analyses did not reveal significant

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associations between neighbourhood SES and preterm birth, low birth weight, macrosomia, SGA, or LGA. Results from logistic regression analyses are presented in Table 2.

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Results of serial mediation models (Figure 1) for all indirect pathways are reported in Table 3. After adjusting for covariates (annual household income, maternal education, ethnicity, maternal age, parity, marital status, gestational age), the effects of neighbourhood SES on

macrosomia and LGA through pre-pregnancy BMI were significant (CIs did not cross zero),

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whereby lower neighbourhood SES was associated with higher pre-pregnancy BMI, and in turn,

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increased risk of macrosomia and LGA. Mediating effects through pre-pregnancy BMI and gestational weight gain together were also significant for preterm birth, macrosomia and LGA. Pairwise contrast comparisons between indirect effects in each model revealed effects through pre-pregnancy BMI alone were significant over and above effects through pre-pregnancy BMI and gestational weight gain together for macrosomia and LGA.

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Discussion

Results from the current study support an extensive body of literature demonstrating associations between neighbourhood SES and birth outcomes, and further suggest that these

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associations may be mediated by maternal weight. Our findings suggested that women residing

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in lower SES neighbourhoods were more likely to be of higher pre-pregnancy BMI, which in turn was associated with increased risk of macrosomia and LGA. Lower neighbourhood SES was also associated with increased risk of preterm birth through pre-pregnancy BMI and gestational weight gain together. Finally, our findings demonstrated that effects observed through prepregnancy BMI alone on macrosomia and LGA were significant over effects observed through pre-pregnancy BMI and gestational weight gain together, suggesting that pre-pregnancy BMI 13

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may influence associations between neighbourhood SES and indices of higher infant weight (macrosomia, LGA) above the effects of gestational weight gain.

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Although past studies have proposed and examined mechanisms underlying associations between neighbourhood SES and birth outcomes, empirical evidence demonstrating maternal weight status as a pathway through which the neighbourhood socioeconomic environment can influence birth outcomes is lacking. Associations between lower neighbourhood SES and

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increased risk of obesity are well-established, and risk is often greater in women than in men

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(Sellström et al., 2009; Stafford et al., 2010). Furthermore, associations between higher pregravid weight and indices of higher infant weight (macrosomia, LGA) are consistently reported (Bodnar et al., 2010; Frederick et al., 2008; Haugen et al., 2014; Nohr et al., 2009; Smith et al., 2007; Stotland et al., 2006). These associations have become more pronounced over the past quarter century (Kramer et al., 2002; Surkan et al., 2004), reflecting the growing need to address the

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trends toward increased maternal and child weight. In the context of perinatal health, identifying modifiable factors that can reduce the risk of poor weight-related outcomes in both mother and infant is critical. Women of higher pre-pregnancy BMIs who reside in lower SES

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neighbourhoods may be particularly susceptible to these poor outcomes, and as such, represent a

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critical yet underserved target for intervention. Associations between neighbourhood SES and low birth weight and SGA through

maternal weight were not observed. We hypothesize that other mechanisms unexplored in the current study may drive these associations; for example, findings from Schempf and colleagues (2009) suggest that behavioural factors (e.g., smoking, drug use, prenatal care) influence associations between the neighbourhood environment and low birth weight. For macrosomia and 14

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LGA, the current study found that mediating effects through gestational weight gain were not significant over and above those observed through pre-pregnancy BMI alone, suggesting that

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pre-pregnancy weight status may drive associations with infant weight above the effects of weight gain in pregnancy. However, associations between neighbourhood SES and preterm birth operated through both maternal weight factors together, suggesting that high weight gain in pregnancy can still confer risk when coupled with high pre-pregnancy BMI. A significant

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number of women in the sample gained an excessive amount of weight in pregnancy; factors

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influencing increased odds of excessive gestational weight gain included lower neighbourhood SES and higher pre-pregnancy BMI. A growing body of evidence suggests that women who gain an excessive amount of weight in pregnancy are also more likely to retain weight for several years postpartum (Vesco et al., 2009; Widen et al., 2015). Targeting interventions that a) reduce pre-pregnancy BMI to normal ranges, and b) limit gestational weight gain to minimize risk of

reproductive years.

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postpartum weight retention may mitigate risk of poor birth outcomes throughout the

The adverse birth outcomes assessed in the current study can carry short- and long-term

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consequences for both mother and child. Infants born preterm are at an increased risk of

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postnatal growth failure, cognitive impairments, and school and behavioural difficulties, even after adjustments for socioeconomic status (Anderson and Doyle, 2003; Hack et al., 2002, 1995). Preterm birth has also been linked to additional costs to new parents (Petrou et al., 2001), and greater risk of the child experiencing socioeconomic inequalities and poorer mental and physical health outcomes later in life (Moster et al., 2008; Saigal et al., 2016). During delivery, macrosomia is associated with prolonged labours, Caesarean delivery, postpartum hemorrhage, 15

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shoulder dystocia, and newborn asphyxia (Henriksen, 2008). In infancy, macrosomia is associated with increased risk of hypoglycemia, hyperbilirubinemia, and respiratory distress

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syndrome (Esakoff et al., 2009). Finally, macrosomia and LGA are associated with increased risk of obesity in childhood and adulthood (Boney et al., 2005; Hermann et al., 2010; Leddy et al., 2008; Oken et al., 2007), which in turn is associated with a myriad of poor health

consequences ranging from metabolic syndrome and Type 2 diabetes to even cancer (Ahlgren et

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al., 2007; Boney et al., 2005; Hermann et al., 2010; Sandhu et al., 2002).

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Limitations and Future Directions

The major strength of the current analysis is the use of prospective data to evaluate the mediating effects of maternal weight on associations between neighbourhood SES and birth outcomes temporally. Another strength of the current study is use of a continuous measure of SES compiled using the smallest spatial unit for which census data is aggregated (DA-level) to

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limit clustering effects. However, several limitations of the current study should be noted. First, compared to other epidemiological investigations into relations between neighbourhood SES and birth outcomes, our sample size is modest. A majority of studies investigating the associations

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between neighbourhood SES and adverse birth outcomes have used databases comprising tens to

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hundreds of thousands of singleton births (Généreux et al., 2008; Liu et al., 2010; Luo et al., 2006, 2004). The advantage of larger datasets is the ability to assess severe outcomes ranging from neonatal mortality to severe preterm births (<28 weeks), which could not be conducted in the current sample due to limited power; this may also explain why associations between neighbourhood SES and adverse birth outcomes were not observed until mediation was evaluated. In addition, the study sample is limited to two census metropolitan regions. Although 16

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our results are generalizable to child-bearing women in these regions, they may not extend to the greater Canadian population or to regions abroad. When contrasted with the Canadian

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population, differences in the current cohort include a lower proportion of visible minorities (Statistics Canada, 2013), a higher average income (Statistics Canada, 2013), and a higher

average maternal age (Ackah and Wang, 2011). Although the disparities noted in our sample may not be wholly representative of those existing in the Canadian population, observing

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disparities that persist in a sample of women with relatively high SES indicates that the influence

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of neighbourhoods are detectable and impactful even within a relatively affluent and educated sample.

Furthermore, although associations between smoking and poor birth outcomes are wellestablished, prevalence of smoking during pregnancy in the current sample was low (3.7%), and not conducive to assessments of smoking as a behavioural mechanism or confounder. A possible

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reason for low prevalence of smoking in our sample may be due to assessment of smoking through self-report; women may be hesitant to report engaging in socially undesirable behaviours, particularly during pregnancy. Shipton and colleagues (2009) suggested that

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underreporting may be more pronounced in women who reside in deprived areas; nonetheless, as

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our sample is relatively high-SES, this bias may not be as pronounced. The current analysis is also limited by use of the voluntary National Household Survey, as rates of non-response on the latter survey may vary on a number of factors including geographic location, socioeconomic status, and ethno-cultural origin. Although responses were weighted to generate a representative sample, the voluntary nature of the survey remains prone to non-response bias; as a result, socioeconomic disparities reported using this information may underreport the extent of 17

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disparities that exist in the assessed regions. Findings may also remain prone to clustering effects as the correlation within DAs was not accounted for; as such, the 95% confidence intervals

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presented in logistic regression and mediation analyses may be anti-conservative, and thus too narrow (Hanley et al., 2003). Finally, we cannot rule out the likelihood of residual confounding in the current study stemming from: dichotomizing sociodemographic variables due to small cell counts; lack of adjustment for factors related to maternal weight (e.g., physical activity, diet) in

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mediation analyses; and/or adjustment for variation in gestational weight gain by length of

(low birth weight, macrosomia).

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gestation by inputting gestational age at delivery as a covariate for non-GA dependent outcomes

In the future, researchers may seek to assess other potential pathways underlying associations between neighbourhood SES and birth outcomes. These pathways may comprise individual-level factors (e.g., maternal mental health, stress, health behaviours) or factors at the

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neighbourhood level (e.g., accessibility to services, pollution). In particular, the identification of maternal weight as a pathway underlying relations between neighbourhood SES and adverse birth outcomes in the current study suggests maternal nutrition may be an area that would benefit

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from further inquiry. As research has implicated the neighbourhood food environment in

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disparities regarding both pre-pregnancy BMI and gestational weight gain (Farley et al., 2006; Wang et al., 2007), future studies in this area may seek to include an assessment of the individual and neighbourhood food environments to better inform health practice and policy. Future studies conducted in Canada can also benefit from use of the long-form census from 2016 onwards due to its recent reinstatement. Finally, replication and extension of the current study in a lower-SES, diverse sample can allow for greater generalizability of our findings, particularly in populations 18

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who are impacted most by factors that may influence associations between lower neighbourhood SES and maternal weight (e.g., accessibility, cost of living, access to and use of health care

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services). Conclusions

Results from the current study suggest that maternal weight mediates the associations between neighbourhood SES and risk of preterm birth, macrosomia and LGA births, independent

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of individual-level sociodemographic factors. The implication of maternal weight as a significant

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mechanism underlying these associations provides support to a growing body of literature assessing modifiable mechanisms that may drive associations between the neighbourhood environment and adverse birth outcomes. Future research should continue to identify and empirically investigate mechanisms underlying associations between the neighbourhood environment and neonatal health to better inform prevention and intervention efforts and

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minimize the short- and long-term risks associated with poor birth outcomes.

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Appendix

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Recommended Total Weight Gain in Pregnancy (Adapted from: Health Canada, 2010; Rasmussen & Yaktine, 2009) Recommended total weight gain (kg)

Recommended total weight gain (lb)

< 18.5

12.5-18

28-40

18.5-24.9

11.5-16

25-35

25-29.9

7-11.5

15-25

≥ 30.0

5-9

11-20

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Pre-Pregnancy BMI

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Acknowledgements: This work was supported by grants from Alberta Innovates Health Solutions and the generous donors of the Alberta Children's Hospital Foundation (L.T-M.). The sources of funding had no role in the study design; in the collection, analysis or interpretation of data; in writing the manuscript; or in the decision to submit the manuscript for publication. The authors gratefully acknowledge the participants of the Alberta Pregnancy Outcomes and Nutrition (APrON) study and the support of the APrON Study Team, whose individual members are B.J. Kaplan, C.J. Field, D. Dewey, R.C. Bell, F.P. Bernier, M. Cantell, L.M. Casey, M. Eliasziw, A. Farmer, L. Gagnon, G.F. Giesbrecht, L. Goonewardene, D.W. Johnston, L. Kooistra, N. Letourneau, D.P. Manca, J.W. Martin, L.J. McCargar, M. O'Beirne, V.J. Pop, and N. Singhal.

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Table 1. Socioedemographic characteristics of the study sample and associations between sociodemographic characteristics and neighbourhood SES Neighbourhood SES Score Mean (SD) -

24.18 (4.84) 15.23 (6.00)

Correlation -.17

Odds Ratio2 (95% CI) -

p <.001

-

.11 .02

-

<.001 .52

79.6 20.4

-.05 (.57) .19 (.71)

-

1 (reference) 1.84 (1.54-2.18)

<.001

96.0 4.0

-.01 (.60) .22 (.77)

-

1 (reference) 1.72 (1.24-2.38)

<.001

87.4 12.6

-.05 (.58) .30 (.67)

-

1 (reference) 2.34 (1.91-2.88)

<.001

55.2 22.2 13.5 5.9 3.2

-.15 (.53) .08 (.61) .20 (.64) .34 (.71) .47 (.75)

-

1 (reference) 1.33 (1.12-1.57) 1.77 (1.45-2.16) 2.26 (1.73-2.94) 2.76 (1.98-3.85)

<.001 <.001 <.001 <.001

.01 (.61) -.04 (.61)

-

1 (reference) .88 (.76-1.02)

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1

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57.2 42.8

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Maternal age Maternal weight factors Pre-pregnancy BMI Gestational weight gain (kg) Ethnicity Caucasian Minority Marital Status Married/Common Law Single/Separated/Divorced Education > High school ≤ High school Household income $100K+ $70K-99,999 $40K-69,999 $20K-39,999 <$20K Parity 0 1+

Mean (SD) or Frequency (%) 31.51 (4.57)

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Note. 1Pearson correlation coefficients and p-values provided for associations between continuous sociodemographic variables and neighbourhood SES. 2Crude odds ratios presented for logistic regression analyses between neighbourhood SES and categorical/binary sociodemographic variables. SD = standard deviation, CI = Confidence Interval, BMI = Body Mass Index.

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Table 2. Odds ratios for associations between neighbourhood SES on birth outcomes

Odds Ratio

LLCI

ULCI

p

Preterm birth

.913

..638

1.306

.618

Low birth weight

1.134

.758

1.697

.540

Small for gestational age

.911

.677

1.225

.537

Macrosomia

1.134

.840

1.531

.411

Large for gestational age

.872

.607

1.253

.460

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Note. LLCI = lower limit confidence interval, ULCI = upper limit confidence interval. BMI = Body Mass Index. The covariates of individual education, income, age, parity, and ethnicity were held constant in all analyses.

2

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Table 3. Effects of neighbourhood socioeconomic status on birth outcomes through pre-pregnancy BMI and gestational weight gain

Macrosomia1

Large for gestational age

ULCI

1: BMI 2: BMI 3: GWG Total

.0570 .0105 -.0113 .0562

.0326 .0058 .0129 .0348

-.0026 .0014 -.0484 -.0093

.1266 .0246 .0064 .1296

-.0657 -.0050 .0056

.0499 .0124 .0185

-.1780 -.0301 -.0178

.0143 .0190 .0663

Total 1: BMI 2: BMI GWG 3: GWG Total 1: BMI*† 2: BMI GWG* 3: GWG

-.0651 -.0358 .0120 -.0154 -.0417 .1183 -.0247 .0274

.0516 .0338 .0076 .0140 .0358 .0331 .0088 .0251

-.1830 -.1153 -.0004 -.0587 -.1211 .0607 -.0462 -.0173

.0197 .0189 .0300 .0019 .0207 .1896 -.0114 .0845

Total* 1: BMI*† 2: BMI GWG* 3: GWG

.1210 .0565 -.0236 .0196

.0375 .0291 .0083 .0246

.0530 .0040 -.0443 -.0249

.2021 .1186 -.0112 .0731

.0357

-.0135

.1275

Total

GWG

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1: BMI 2: BMI 3: GWG

GWG*

.0525

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Small for gestational age

SE

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Low birth weight1

b

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Preterm birth

Indirect Effect

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Birth Outcomes

3

95% BCa Bootstrap CI

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Note. BCA bootstrap CI = bias-corrected and accelerated bootsrap confidence interval, BMI = pre-pregnancy BMI, GWG = gestational weight gain, b = productof-coefficients estimate of indirect effect, SE = standard error, LLCI = lower limit confidence interval, ULCI = upper limit confidence interval. BMI = Body Mass Index. The covariates of individual education, income, age, parity, and ethnicity were held constant in all analyses. 1The covariate of gestational age was held constant in analyses involving macrosomia and low birth weight. * p < .05; † indirect effect significantly different from other indirect effects in model after pairwise contrast comparisons

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Figure 1. Serial mediation models of the indirect associations between neighbourhood SES and adverse birth outcomes. Standardized regression coefficients are a1, a2, b1, b2, c’, d21 (B panel)

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Research Highlights

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Large body of evidence shows link between neighbourhood SES and birth outcomes Few studies assess pathways underlying these associations Current study suggests neighbourhood effects may operate via maternal weight Findings identify underserved, high-risk group who would benefit from intervention

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