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PLOS ONE logoLink to PLOS ONE
. 2020 Jun 5;15(6):e0233416. doi: 10.1371/journal.pone.0233416

The role of neighbourhood socioeconomic status in large for gestational age

Farid Boubred 1, Vanessa Pauly 2,3, Fanny Romain 2, Guillaume Fond 2,3, Laurent Boyer 2,3,*
Editor: Diane Farrar4
PMCID: PMC7274403  PMID: 32502147

Abstract

Objective

To determine whether neighbourhood socioeconomic status (SES) was associated with large for gestational age (LGA) while considering key sociodemographic and clinical confounding factors.

Setting and patient

All singleton infants whose parents were living in the city of Marseilles, France, between 2013 and 2016.

Method

Population-based study based on new-born hospital birth admission charts from the French National Uniform Hospital Discharge Data Set Database. LGA infants were compared to appropriate-for-gestational-age (AGA) infants. Multiple generalized logistic model analysis was used to examine factors associated with LGA.

Results

A total of 43,309 singleton infants were included, and 4,747 (11%) were born LGA. LGA infants were more likely to have metabolic and respiratory diseases and to be admitted to the neonatal intensive care unit. Multiparity, advanced maternal age, obesity and diabetes were associated with an increased risk of LGA. Lower neighbourhood SES was associated with LGA (aOR = 1.24, 95% CI: 1.14; 1.36; p<0.0001) independent of age, diabetes, obesity, maternal smoking and multiparity. The strength of this association increased with maternal age, reaching an aOR of 1.50 (95% CI: 1.26; 1.78; p<0.0001) for women > 35 years old.

Conclusion

Neighbourhood SES could be considered an important factor for clinicians to better identify mothers at risk of having LGA births in addition to well-known risk factors such as maternal diabetes, obesity and age. The intensification of the association between SES and LGA with increasing maternal age suggests that neighbourhood disadvantage may act on LGA cumulatively over time.

Background

Large for gestational age (LGA) is a growing public health problem in developed countries [13], and the prevalence of infants born LGA varies from 5 to 20% in these countries [4]. Recent studies suggest that the number of LGA infants has increased over the last two decades [2, 5]. The number of LGA births is also expected to rise due to the current epidemic context of obesity and diabetes, two major LGA risk factors [6]. Among women with these metabolic complications, the risk of LGA is increased twofold to threefold [7, 8]. LGA increases the risk of numerous adverse perinatal and birth outcomes, including caesarean delivery, postpartum haemorrhage, shoulder dystocia, perinatal asphyxia, neonatal hypoglycaemia, hyperbilirubinemia and neonatal respiratory distress disorders [911]. LGA also affects long-term health outcomes by increasing the risk for cancer [12]; metabolic disorders including overweight, obesity and type 2 diabetes in children and adults [13]; and cardiovascular disease [14]. A better way to identify pregnant women who are at risk of delivering a LGA infant is of particular concern for improving neonatal health outcomes, interrupting the intergenerational chain of metabolic diseases and preserving offspring health and well-being in adulthood [15].

Several factors, including maternal overweight/obesity, excessive gestational weight gain and gestational diabetes mellitus, are known to induce LGA [16, 17]. LGA may also be influenced by maternal age: advanced maternal age (i.e., over 35 years) has been suggested to increase the risk of LGA [18]. Births to women of advanced maternal age have increased over the past few decades, reaching rates of approximately 10% to 20% of births in high-income countries [19, 20]. This age group of women has increased rates of various complications, such as preeclampsia, gestational diabetes mellitus, low birth weight and perinatal mortality, during pregnancy [2123]. A large body of literature has reported the impact of neighbourhood socioeconomic status (SES) on perinatal and birth outcomes, including low birth weight, small for gestational age (SGA) and preterm birth [2426]. However, the influence of neighbourhood SES on LGA and combined effects with other LGA factors, including maternal age, has received little attention. Neighbourhood SES may affect LGA through the neighbourhood’s effects on maternal behaviours, such as the lack of access to healthy food or opportunities for exercise to combat obesity and diabetes and the low access to high-quality maternity care [27]. To our knowledge, only a few studies in the United States and Canada have addressed this issue with conflicting results, e.g., two studies showing an increased risk of LGA and one study showing a decreased risk of LGA for lower neighbourhood SES [2830]. Further studies are thus needed to confirm the influence of neighbourhood SES on LGA. Explorations of this issue in other countries and health care systems may provide additional information. France may offer an interesting view, as the French Health system offers universal health coverage guaranteeing access to care for pregnant women regardless of cost [31]. The standards of maternity care are considered high, with a total of 7 recommended prenatal examinations. According to the World Health Organization, prenatal examinations provide an important opportunity to prevent and manage concurrent diseases through integrated service delivery [32], and France is one of the European countries with the lowest percentages of late prenatal examinations [33, 34]. Medical costs during the perinatal period are covered at a rate of 100% by the French social security system. Investigating the effect of SES on LGA at the whole population level with universal maternity coverage would therefore be a significant step in the identification of LGA risk factors.

The objective was to determine whether neighbourhood SES was associated with LGA while considering key sociodemographic and clinical confounding factors in a large population-based study using data from the French National Hospital Database system.

Methodology

Study design and data source

This population-based cohort study was based on new-born hospital birth admission charts from the French National Uniform Hospital Discharge Database (Programme de Médicalisation du Système d’Information, PMSI), which systematically collects administrative and medical information, including obstetrical characteristics. This database is compiled based on diagnosis-related groups, and each diagnosis (both maternal and new-born infant) is coded according to the International Classification of Diseases, Tenth Revision (ICD-10) [35]. Infants were classified using diagnosis-related groups related to births. Only singleton births were included in the analysis. The charts of infants with errors on individual linkage or without a maternal address code were excluded. All singleton infants whose parents were living in the city of Marseilles, France, between 2013 and 2016 were eligible for the study regardless of the maternity ward address. This study was part of a regional project aimed at identifying factors influencing perinatal health outcomes. Marseille is the second-largest city of France, with almost 860,000 inhabitants and 13,000 births per year and is one of the French cities with the highest levels of social inequality [36]. The size of the city was sufficient to use a geographic socioeconomic index based on zip codes. Previous studies in Marseilles reported that social inequalities were well captured by zip codes, i.e., inhabitants shared resembling socioeconomic characteristics in the same zip code [37], which has not been demonstrated in other French cities or in studies associating urban and rural populations.

From the PMSI database, we selected all in-hospital stays corresponding to a neonatal diagnosis-related group (DRG n°15 in France and with an age equal to 0 days) and with address codes localized to Marseilles. We excluded in-hospital stays with linking problems, transfers from hospitals in another geographical area, infants from multiple pregnancies or when the number of infants was not specified, and infants who were small for gestational age (birth weights lower than the 10th percentile). Then, we defined two populations: infants who were LGA (birth weights lower than the 10th percentile) and controls who were appropriate for gestational age (AGA).

Neighbourhood SES

For each birth, maternal SES was evaluated using the available neighbourhood deprivation index (NDI) developed in France (French Deprivation index, FDep09) based on the residential zip code [38]. The NDI was developed from variables included in the 2009 national census data published by the French National Institute of Statistics and Economic Studies (INSEE, Institut National de la Statistique et des Etudes Economiques) and was calculated for each district in Marseille (n = 16). The NDI was used as a proxy for the social environment and was the first component used in a principal component analysis involving four socioeconomic variables: median household income, percentage of the population aged 15 years or older that graduated from high school, percentage of blue-collar workers and the unemployment rate of the population aged between 15 and 64 years. The NDI was categorized according to quartiles, from the least (NDI level 1) to the most deprived area (NDI level 4). This index was adjusted to the study period and validated by variables from the national 2015 Census data (S1 Table).

Maternal and neonatal data collection

Maternal and neonatal characteristics were derived from the infant in-hospital stay. Obstetrical data were included in the new-born hospital birth admission chart. Gestational age, birth weight, admissions to the neonatal intensive care unit (NICU) and data on some neonatal morbidities, including metabolic disorders (ICD-10 codes P700, 701, 711, 709, and 719), jaundice (ICD-10 codes P599, P58, and P590), respiratory diseases (ICD-10 codes P22, P24, and P293), perinatal asphyxia with neonatal encephalopathy (ICD-10 codes P21 and P916) and neonatal death up to day 28, were collected. Preterm birth was defined as gestational age less than 37 weeks. SGA, AGA and LGA were defined as birth weights lower than the 10th percentile, within the 10th and 90th or higher than the 90th percentile, respectively, based on the national French growth chart(19).

Data on the following maternal comorbidities were also searched in the infant in-hospital stay: maternal hypertension or preeclampsia (ICD-P000); gestational diabetes mellitus, including maternal diabetes and gestational diabetes mellitus codes (ICD-P700 and P701); obesity (ICD-10 code E66*: body mass index greater than 30); multiparity; mode of delivery (ICD-10 code P034 for caesarean section); and maternal smoking, through the proxy ICD 10 code P042, which is ‘New-born affected by maternal use of tobacco.’ Due to coding instructions, parity is only available for vaginal deliveries. Data on maternal age were collected after linkage with maternal hospital admission charts. For infants with unavailable linkage, we replaced the corresponding maternal age with the mean age of the mothers who delivered in the same maternity ward in the same year.

Statistical analysis

Descriptive data for sociodemographic and clinical characteristics are presented as frequencies and percentages. First, we described and compared the characteristics of the LGA vs AGA infants as well as the characteristics according to the NDI (Table 1). Univariate analyses were performed using a generalized logistic model with the hospital or maternity ward as a random effect to take into account correlations of data due to hospital clustering (Table 2). To determine whether the NDI was an independent factor associated with LGA, we performed stepwise logistic regression analyses. Because data on maternal age was absent for 10% of women, we performed an analysis with imputation to the mean (i.e., replacement of maternal age by the mean age of the mothers who delivered in the same maternity ward in the same year) and an analysis excluding mothers with missing data. Interaction effects were tested between the different variables, particularly the interaction between maternal age and NDI levels presented in Fig 1. Gestational diabetes mellitus and obesity were not included in the same model because of multicollinearity. Because multiparity data were only available for vaginal deliveries, another model for sensitivity analysis was computed only on vaginal delivery data to include multiparity in the analysis. All multivariate analyses are presented in Table 3. Odds ratios (ORs) with 95% confidence intervals (95% CIs) were calculated. Statistical significance was defined as p<0.05. The statistical analysis was performed with SAS 9·4 (SAS Institute), and a univariate/multivariate generalized logistic regression model was performed using PROC GLIMMIX in SAS®.

Table 1. Maternal and neonatal characteristics of appropriate-for-gestational-age (AGA) and large-for-gestational-age (LGA) infants.

Perinatal characteristics Total population LGA AGA p
(n = 43,309) (n = 4747) (n = 38,562)
Obstetrical data
Maternal age, n (%) < 0.0001
< 25 y 10,781 (24.9) 1054 (22.2) 9727 (25.2)
25–30 y 11,362 (26.2) 1175 (24.7) 10,187 (26.4)
30–35 y 12,449 (28.7) 1392 (29.3) 11,057 (28.7)
35 y 8717 (20.1) 1126 (23.7) 7591 (19.7)
Obesity, n (%) 2108 (4.9) 385 (8.1) 1723 (4.5) <0.0001
Multiparity*, n (%) 18,672/31,858 2183/2983 16,489/28,875 <0.0001
(58.6) (73.2) (57.1)
GDM**, n (%) 2794 (6.4) 643 (13.5) 2151 (5.6) <0.0001
Preeclampsia, n (%) 777 (1.8) 99 (2.1) 678 (1.8) 0.11
Maternal smoking, n (%) 629 (1.4) 29 (0.6) 600 (1.5) <0.0001
Caesarean delivery, n (%) 8967 (20.7) 1513 (31.8) 7454 (19.3) <0.0001
Neonatal data
GA, mean (SD) 39.1 (1.6) 39.1 (1.7) 39.1 (1.6) 0.81
Preterm birth, n(%) 2156 (4.9) 1909 (4.9) 207 (5.2) 0.45
BW, mean (SD) 3374 (467) 4062 (399) 3289 (400) < 0.001
Sex (female), n (%) 20,864 (48.2) 2340 (49.3) 18,524 (48) 0.10
Congenital malformations, n(%) 2705 (6.2) 330 (6.9) 2375 (6.1) 0.07
NICU admissions, n (%) 1993 (6.4) 304 (6.4) 1689 (4.4) <0.001
Metabolic disorders***, n (%) 3021 (7) 691 (14.5) 2330 (6) <0.001
Jaundice, n (%) 3086 (7.1) 341 (7.2) 2745 (7.1) 0.92
Respiratory disease, n (%) 3045 (7) 450 (9.5) 2595 (6.7) <0.001
Perinatal asphyxia, n (%) 1675 (3.9) 189 (4) 1489 (3.7) 0.42
Neonatal deaths, n (‰) 45 (1.03) 7 (1.47) 38 (0.98) 0.32
Maternal SES
NDI, n (%) <0.001
1 least deprived 11,845 (27.3) 1181 (24.9) 10,664 (27.6)
2 9702 (22.4) 1045 (22) 8657 (22.4)
3 9218 (21.3) 975 (20.5) 8243 (21.4)
4 most deprived 12,544 (28.9) 1546 (32.6) 10,998 (28.5)

BW: birth weight (g); GA: gestational age (weeks); GDM: gestational diabetes mellitus; metabolic disorders: hypoglycaemia and hypocalcaemia; NICU: neonatal intensive care unit; NDI: neighbourhood deprivation index.

*Multiparity was calculated for infants delivered vaginally (data not available for caesarean delivery).

**GDM: gestational diabetes mellitus.

***metabolic disorders: hypoglycaemia and hypocalcaemia

Table 2. Perinatal characteristics of the population according to the neighbourhood deprivation index.

  Neighbourhood deprivation index  
Perinatal characteristics Q1 Q2 Q3 Q4 p
Least deprived Most deprived
(n = 11,845) (n = 9702) (n = 9218) (n = 12,544)
Maternal data
Maternal age, n (%)         <0.001
< 25 y 2174 (18.3) 2222 (22.9) 2653 (28.8) 3732 (29.7)
25–30 y 2892 (24.4) 2720 (28) 2293 (24.9) 3457 (27.5)
30–35 y 4036 (34.1) 2867 (29.5) 2491 (27) 3055 (24.3)
35 y 2743 (23.1) 1893 (19.5) 1781 (19.3) 2300 (18.3)
Obesity, n (%) 205 (1.7) 444 (4.6) 570 (6.2) 889 (7.1) <0.001
Multiparity*, n (%) 4857/9147 4034/7241 3877/6527 (59.4) 5904/8943 (66) <0.001
(53.1) (55.1)
Gestational diabetes mellitus, n (%) 475 (4) 573 (5.9) 633 (6.9) 1113 (8.9) <0.001
Preeclampsia, n (%) 170 (1.4) 192 (1.9) 168 (1.8) 247 (1.9) 0.005
Maternal smoking, n (%) 92 (0.8) 188 (1.9) 175 (1.9) 174 (1.4) <0.001
Caesarean delivery, n (%) 2146 (18.1) 1957 (20.2) 1893 (20.5) 2639 (21) <0.001
Neonatal data
GA, mean (SD) 39.20 (1.5) 39.13 (1.6) 39.16 (1.6) 39.08 (1.7) <0.001
BW, mean (SD) 3372 (449) 3365 (467) 3378 (458) 3379 (489) 0.33
Preterm birth, n (%) 546 (4.6) 501 (5.1) 425 (4.6) 684 (5.4) < 0.01
Sex (female), n (%) 5666 (47.8) 4672 (48.1) 4453 (48.3) 6073 (48.4) 0.82
LGA, n (%) 1181 (9.9) 1045 (10.8) 975 (10.6) 1546 (12.3) <0.001
LGA with diabetes mellitus/GDM, n (%) 110 (0.9) 136 (1.4) 137 (1.5) 260 (2.1) <0.001
Congenital malformations, n (%) 755 (6.4) 655 (6.7) 600 (6.5) 695 (5.5) 0.001
NICU admissions, n (%) 433 (3.7) 509 (5.2) 425 (4.6) 626 (5) <0.001
Neonatal death, n (‰) 9 (0.76) 9 (0.97) 9 (0.97) 18 (1.43) 0.3

BW: birth weight (g); GA: gestational age (weeks); GDM: gestational diabetes mellitus; NICU: neonatal intensive care unit; Neonatal death: death during the first 28 days of life.

*Multiparity: calculated for infants delivered vaginally.

Fig 1. LGA, neighbourhood deprivation index (NDI) and maternal age.

Fig 1

The rate of infants born LGA increased gradually with advancing maternal age, and the NDI reached the highest level among infants born to mothers aged 35 years and living in the most deprived neighbourhoods.

Table 3. Factors associated with LGA delivery.

Odds ratios (95% CI) from the stepwise logistic regression models.

  Model with imputation of maternal age* N = 43,309 Model without imputation of maternal age N = 38,547 Model with obesity instead of GDM N = 38,547 Model including multiparity only for vaginally delivered infants N = 31,858
  aOR (95% CI) P aOR (95% CI) P aOR (95% CI) P aOR (95% CI) P
Maternal age <0.001 <0.001 <0.001 <0.01
< 25 y 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference]
25–30 y 1.06 (0.97 - 1.16) 0.17 1.40 (1.25 - 1.57) <0.001 1.43 (1.27 - 1.60) <0.001 1.26 (1.10 – 1.44) <0.001
30–35 y 1.15 (1.05 - 1.26) 0.002 1.52 (1.36 -1.70) <0.001 1.59 (1.42 -1.77) <0.001 1.20 (1.05 – 1.37) <0.01
> 35 y 1.28 (1.17 - 1.41) <0.001 1.70 (1.51 -1.91) <0.001 1.83 (1.63 -2.05) <0.001 1.24 (1.07 - 1.42) <0.01
Gestational diabetes mellitus 2.61 (2.36 - 2.88) <0.001 2.49 (2.24 - 2.76) <0.001 - - 1.92 (1.67 - 2.20) <0.001
Obesity - - - - 1.87 (1.66 - 2.12) <0.001 - -
Multiparity - - - - - - 1.95 (1.79 - 2.14) <0.001
Maternal smoking 0.38 (0.26 - 0.55) <0.001 0.37 (0.25 - 0.55) <0.001 0.37 (0.25 - 0.54) <0.001 0.38 (0.23 - 0.61) <0.001
NDI   <0.001 <0.001 <0.001 0.047
1 least deprived 1 [Reference] 1 [Reference] 1 [Reference] 1 [Reference]
2 1.08 (0.99 - 1.18) 0.08 1.12 (1.02 - 1.23) 0.03 1.13 (1.02 - 1.24) 0.015 1.12 (1.00 - 1.18) 0.04
3 1.08 (0.96 - 1.16) 0.24 1.11 (1.00 - 1.23) 0.05 1.12 (1.01 - 1.23) 0.036 1.05 (0.93 - 1.18) 0.39
4 most deprived 1.24 (1.14 - 1.36) <0.001 1.28 (1.16 - 1.41) <0.001 1.28 (1.16 - 1.41) <0.001 1.15 (1.03 - 1.29) 0.01
Goodness of fit (AIC) 29.606 25.864 26.040 19.364

NDI: neighbourhood deprivation index; aOR: adjusted OR.

*A significant interaction was found between maternal age and NDI: The strength of the association between SES and LGA increased with maternal age, reaching an aOR of 1.50 (95% CI: 1.26; 1.78; p<0.0001) for women older than 35 years in ND4.

Ethics

Our institution (Assistance Publique—Hôpitaux de Marseille) was granted access to the French National Uniform Hospital Discharge Data Set Database by the ATIH (Agence Technique d’Information sur l’Hospitalisation https://www.atih.sante.fr/), the organization in charge of this database in France, in compliance with French law after a deliberation of the French Commission for Data Protection and Liberties (CNIL). In addition, research on retrospective data is excluded from the framework of the French Law Number 2012–300 of 5 March 2012 relating to the research involving human participants, as modified by the Order Number 2016–800 of 16 June 2016. Neither French competent authority (Agence Nationale de Sécurité du Médicament et des Produits de Santé, ANSM) approval nor French ethics committee (Comités de Protection des Personnes, CPP) approval is required in this context.

Results

The study period included 47,331 singleton infants after exclusion of 4,341 infants’ charts due to errors or missing data, the absence of the mother’s address code, and multiple pregnancies. After the exclusion of 4,022 infants for SGA, 43,309 were included in this study, of which 4,747 (10.9%) were LGA and 38,562 were AGA (Fig 2). The linkage between mother and infants was available for 90% of the infants. The obstetrical and neonatal characteristics of the study population are detailed in Table 1. The rates of infants born to mothers of advanced age (i.e., over 35 years), gestational diabetes mellitus and preeclampsia were 20.1%, 6.4% and 1.8%, respectively. Approximately 5% of the infants in the study population were born preterm (gestational age less than 37 weeks).

Fig 2. Flowchart.

Fig 2

LGA population characteristics

The comparison of AGA and LGA infants is presented in Table 1. In comparison with AGA infants, LGA infants were more likely to be born to older (maternal age above 35 years) and multiparous mothers (p<0.0001). The rate of gestational diabetes mellitus was 2.5-fold higher (p<0.0001) among LGA infants than among AGA infants. In contrast, the rate of infants born to tobacco-smoking mothers was 3-fold lower (p<0.0001) in the LGA group. While the mean GA and preterm birth rate were comparable between groups, NICU admissions were significantly higher for LGA infants than for AGA infants (p<0.0001), with higher rates of hypoglycaemia, hypocalcaemia and respiratory disorders (p< 0.0001).

Neighbourhood deprivation and perinatal outcomes

Perinatal outcomes according to the NDI are detailed in Table 2. Approximately 30% of the population lived in the most deprived neighbourhoods (NDI level 4). The proportion of LGA varied by NDI level (12.3% in NDI level 4 vs 9.9% in NDI level 1, p <0.0001). Infants born to mothers living in the most deprived areas were more likely to be born preterm (p<0.001). Infants from NDI levels 2, 3 and 4 were more frequently exposed to maternal smoking than those from NDI level 1 (p < 0.001). In contrast, in less deprived neighbourhoods, infants were more likely to be born to older (p<0.0001) and nulliparous (p<0.0001) mothers. The mean GA decreased with NDI, and the mean birth weight was unchanged due to a higher rate of LGA. Similarly, gestational diabetes mellitus (p<0.0001) and LGA associated with gestational diabetes mellitus (p<0.01) were 2-fold higher in NDI level 4 than in NDI level 1.

Factors associated with LGA

Multivariate analyses are detailed in Table 3. NDI, advanced maternal age (maternal age ≥ 35 years), gestational diabetes mellitus and obesity were significantly associated with LGA (p<0.001). Infants born to mothers living in deprived neighbourhoods (NDI level 4) had 25% higher odds of being LGA (aOR = 1.24, 95% CI: 1.14; 1.36; p<0.0001) after adjustment for maternal age, gestational diabetes mellitus, obesity, maternal smoking and multiparity. The strength of the association between SES and LGA increased with maternal age, reaching an aOR of 1.50 (95% CI: 1.26; 1.78; p<0.0001) for women older than 35 years in NDI level 4 (significant interaction). Fig 1 illustrates how LGA varies according to maternal age and the NDI. The differences in the rate of LGA between NDI levels 1 and 4 increased with advanced maternal age, with differences ranging from 4% to 54% in mothers under the age of 30 years relative to those aged 35 years and older (p < 0.001). SES was still significant after the inclusion of multiparity in the sensitivity analysis performed only on vaginal deliveries (n = 31,858), with an aOR = 1.15 (95% CI: 1.03–1.29, p < 0.001); multiparity was associated with LGA with an aOR = 1.19 (95% CI: 1.79–2.14). Maternal smoking was associated with lower odds of LGA (p<0.001).

Discussion

Main findings

The present findings can be summarized as follows. Between 2013 and 2016, 47,331 singleton infants whose parents were living in the city of Marseille were identified in the French National Hospital Database system. The prevalence of LGA varied according to neighbourhood SES, reaching 12% in the most deprived neighbourhoods. Lower neighbourhood SES was associated with LGA independent of maternal age, gestational diabetes mellitus, obesity, maternal smoking and multiparity. The strength of this association increased with maternal age, reaching an aOR of 1.50 for women older than 35 years of age.

Interpretation

The first important finding is the significant association between neighbourhood SES and LGA in a large population-based study, complementing the previous results on the effect of SES on other perinatal outcomes such as low birth weight, SGA and preterm birth [16, 39, 40]. To date, the association between SES and LGA has rarely been investigated, especially in industrialized countries with developed health care systems. Our findings confirm the results of two previous studies [28,30] in another socioeconomic and cultural contexts and with a large population but contradict the results of Shankardass et al [29], which did not adjust their findings to potential confounding characteristics, such as in our study. Importantly, this association remained significant after adjustment for metabolic disturbances, suggesting that neighbourhood SES could be considered relevant information for clinicians to better identify mothers at risk of having LGA births in addition to well-known risk factors such as gestational diabetes mellitus, maternal obesity and advanced maternal age. The association between neighbourhood SES and LGA may be explained by mechanisms such as poor health behaviours, endocrine disruptors and race/ethnicity characteristics. Future studies should explore the association between neighbourhood SES and inadequate diets (e.g., micronutrient and vitamin deficiencies [41]), endocrine disruptor exposure and ethnic characteristics. For ethical concerns, it is not possible to record ethnicity in the French databases. However, some previous studies have suggested that gypsy, Romanian and sub-Saharan migrant children may be at higher risk of LGA [42].

The LGA rate in deprived neighbourhoods was 25% higher than that in less deprived neighbourhoods. This finding confirms that LGA remains an important health disparity and that universal health coverage is not sufficient to counter the multiple factors that contribute to health and health care disparities [43], namely, the levels and distribution of income, education, housing, nutrition and safety [43, 44, 45]. In accordance with this finding, a previous French study performed in Paris reported insufficient healthcare follow-up among women with low SES [46]. Future studies should better explore economic, social, or environmental disadvantages, and targeted interventions should be proposed to reduce health disparities in these deprived neighbourhoods [47].

Finally, we found a significant interaction between maternal age and neighbourhood SES on the risk of LGA. Advanced maternal age is known to adversely affect birth and neonatal outcomes, but its effects on LGA remain poorly explored [2123]. Kenny LC et al. reported an increase in LGA births among older women (> 30 years), and as we observed in our study, they found larger effects among women living in deprived neighbourhoods [18]. The underlying mechanism is unclear. Although we do not have information on the duration of residence, it may be reasonably hypothesized that LGA risk may increase with the duration of exposure to the abovementioned SES-associated factor. This hypothesis could be supported by previous studies on the life-course accumulation of neighbourhood disadvantage and its impact on health [48]. It can be speculated that older mothers with low SES have a cumulative disease risk over time, including repeated adverse health events that are stressful, diabetes, obesity and multiparity, which in turn promote LGA [3]. An association has been reported between older mothers and low SES and several risky behavioural factors, such as psychosocial issues, smoking practices, alcohol exposure, and stress, which should be specifically targeted early in pregnancy [4951]. If this cumulative risk hypothesis were to be confirmed, our results would suggest that preventive efforts in current and individual conditions are not sufficient and that early health and social support (individual and/or collective) interventions are also necessary to improve perinatal and birth outcomes.

Strengths and limitations

The French National Uniform Hospital Discharge Data Set Database does not include some relevant individual factors, including educational status, ethnicity, maternal BMI before or in early pregnancy, maternal body weight gain, hyperglycaemic oral test, maternal nutritional status and data regarding foetal growth. Thus, we were unable to identify how these factors and their severity may mediate the effects of neighbourhood deprivation on LGA. Some comorbidities and clinical characteristics derived from ICD codes could be underreported, such as maternal smoking or obesity. In addition, we did not analyse congenital defects that could affect pregnancy and infant size at birth. Specific studies should explore this issue. Such limitations may nevertheless be mitigated by the fact that our study is the first large population-based study performed in industrialized countries. Our data were extracted from the French national database, which accurately registers hospitalized new-borns, maternal health data and the domicile postal code of the mother’s address. All consecutive deliveries in private or public hospitals providing different levels of health care were collected in this registered database system, which strengthens the findings of the study. Last, the accuracy of geographical methods based on the residential postal code can nevertheless be put into question in studies including urban and rural populations with nonhomogeneous social neighbourhood characteristics. This study avoided such a bias by limiting investigations to a single large city. However, conducting a study in a single French city may limit the generalizability of our findings. Future studies should thus confirm our findings in other geographical areas.

Conclusions

This study showed that infants born to mothers living in deprived socioeconomic areas had a higher risk of being LGA than those who were born to mothers living in less socioeconomically deprived areas. Neighbourhood disadvantages should be taken into account in routine follow-up care of pregnant women as well as other risk factors to prevent LGA deliveries. The intensification of the association between SES and LGA with increasing maternal age also suggests that neighbourhood disadvantage may act cumulatively on LGA. This finding argues for the integration of early health and social support interventions in the most deprived neighbourhoods to reduce LGA.

Supporting information

S1 Table. Comparison of the neighbourhood deprivation index used in the study with related variables from the 2015 French National Census data.

(DOCX)

Data Availability

Data cannot be shared publicly because of this data are issued from French national medicoadministrative database. Data are available from the ATIH Institutional Data Access (contact via https://www.atih.sante.fr/) for researchers who meet the criteria for access to confidential data. The data underlying the results presented in the study are available from ATIH (https://www.atih.sante.fr/).

Funding Statement

The authors received no specific funding for this work.

References

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Decision Letter 0

Diane Farrar

25 Feb 2020

PONE-D-19-26817

The role of neighborhood socioeconomic status in large for gestational age: a nationwide study

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Reviewer #1: 1. Overall evaluation

This study evaluates the correlation between zip-code level neighborhood socioeconomic status (SES) and the risk of large for gestational age (LGA), focusing on newborn hospital birth admission claims in an urban area – Marseille – extracted from nationally representative administrative data called the French National Uniform Hospital Discharge Database. Statistical strategy is straightforward, applying multivariate logistic regression model. The result shows that lower zip-code level of neighborhood SES tend to be associated with LGA significantly, after adjusting for mother’s age, diabetes, obesity, smoking behavior and multiparity. Further, supposed that mother’s age is a proxy of duration of residency, the author(s) discuss that older mothers living in the area with lower SES might experience a cumulative disease risk over time and therefore, the intensification of the correlation between neighborhood SES and LGA increases along with mother’s age.

I fully agreed with the contributions and novelties highlighted by the author(s), such that this study sheds a light on the issue under the universal maternity coverage in a developed country – France – other than Northern American countries; and also it could provide clinicians additional information to help them understand mothers’ risks of LGA births, along with conventional ones, such as maternal diabetes, obesity and age. However, I think that the author(s) had better to explain carefully about the situation in France as a background to persuade international readers. I would like to provide both major and minor comments as follows. Hopefully, they are helpful for the author(s) to improve this paper.

2. Major Comments

2-1. Title

The current title “The role of neighborhood socioeconomic status in large for gestational age: a nationwide study” is unclear about target population to be analyzed. Further, although this study focused only on an urban area in an urban area – Marseille –, the subtitle (“a nationwide study”) might mislead the readers. So, I strongly suggest the author(s) to revised the title to the one which reflects the contents of this study more precisely.

2-2. More detailed explanation may be helpful for international readers to understand the situation in France as a background information

In the section of “BACKGROUND”, the author(s) had better to provide more detailed explanation how serious LGA births has been becoming, not only in France but also in other developed countries, showing some basic statistics (time-trend LGA births, if possible).

Also, the author(s) emphasizes high standards of maternity care in France due to guaranteed access to care regardless of costs for pregnant women in 14th line of page 3, without any reference. So, the author(s) need to show some reference for this. Then, the author(s) had better to show basic statistics and/or to conduct institutional explanation for proving how high the standards of maternity care in France are, compared to other developed countries like OECD countries.

Adding such more detailed clarifications may help to persuade international readers how important this study is.

2-3. Why Marseille?

As long as I noticed, in the section of “METHODOLOGY”, there are no explanations why the author(s) chose Marseille from a nationwide population-based database in France. Then, from the 2nd bottom line in page 9 through page 10, the author(s) briefly mentioned why they chose an urban large city for this study, such that “Lastly, the accuracy of geographical methods based on residential postal code can nevertheless be put into question in studies including urban and rural populations with nonhomogeneous social neighborhood characteristics. This study avoided such a bias by limiting investigations to a single large city”. The author(s) definitely should reallocate this sentence to the “METHODOLOGY” section. Further, the author(s) should clarify the reason why they chose Marseille among multiple urban areas/cities in France and they have to discuss the possible sampling bias issues on the estimation caused by focusing on a large city like Marseille in “DISCUSSION” section.

2-4. Statistical analysis & results

2-4-1. A flowchart for the procedure of extracting the data to be analyzed is necessary

In the first paragraph of “RESULTS” section in page 6, the author(s) describe how to extract the data to be analyzed in this study. I suggest to bring this explanation to “METHODOLOGY” section and show it by a figure of flowchart. It may be very helpful for the readers to understand how the data is constructed.

2-4-2. Justification for the threshold “p-values<0.2”

In the 7-9th line of page 5, the author(s) said “Variables relevant to the model were selected based on a threshold p<0.2”. Please provide the rationale for choosing this threshold. Otherwise, the selection of explanatory variables seems to be with some ‘intention’. If the author(s) do not have any clear justifications for this, they should to some stepwise logistic regression analyses.

2-4-3. Sensitivity analysis excluding/imputing missing data on maternal age

From the bottom line of page 5 through page 6, the author(s) mentioned the above sensitivity analyses. However, I do not see any results for this. Please show the results of sensitivity analysis, at least in supplementary tables. Also, if the author(s) did some imputation for missing maternal age, they should briefly explain which imputation method they used, either in the main body of text or in the appendix.

2-4-4. Test statistics for regression analyses are necessary to be shown in Table 3

Test statistics for validity of the model should be shown, such as log likelihood, pseudo-R2 values, and Akaike's Information Criterion (AIC).

2-4-5. Univariate results are not necessary to be shown in Table 3

In Table 3, the author(s) show both results of univariate and multivariate logistic regression analyses. However, I do not see any meaning to show univariate results (besides, the results do not seem to be different much). Rather, it is more interesting to show the results of supplementary tables 2 and 3. Besides, in page 7, the author(s) explain the results (ORs) of these supplementary tables in detail. For example, the author(s) said “The strength of the association between SES and LGA increased with maternal age, reaching an aOR of 1.59 (95% CI: 1.26; 1.78 p<0.0001)...”, which cannot be seen in any tables in the main body of manuscript.

2-4-6. How to estimate the results for Figure 1 & supplementary table 3

I do not see how to extract the results of Figure 1 (supplementary table 3). I guess the author(s) introduce interactive terms of maternal age and NDI levels into logistic regression analysis. If possible, please clarify the estimation model in “METHODOLOGY” section. Otherwise, the reader must be lost in a series of explanation about the results of Figure 1 in page 7.

3. Minor Comments

(1) In page 4, the translation of the name of dataset sounds a little strange. How about “the French National Uniform Hospital Discharge Data” or “the French National Uniform Hospital Discharge Database”. I do not think that the author(s) need to repeat “Data Set Database”.

(2) In the 8th line of page 5, what is (Audipog) (19)? Please clarify.

(3) In “RESULTS” in page 6, the author(s) said “The rates of infants born to mothers with advanced age (more than 35 years)” is “51%”, which is different from the value (“20.1% for age 35 and older”) shown in Table 1.

(4) Same as (1), In “RESULTS-LGA population characteristics”, the author(s) said “In comparison with AGA infants, LGA infants were more likely to be born to older (maternal age more than 35 years) and multiparous mothers (p<0.0001)”. However, Table 1 shows an opposite result, such that the ratio of Cesarean delivery tends to be significantly larger for LGA infants so that LGA infants were less likely to be born to multiparous mothers.

(5) At the second line from the bottom of page 6, the author(s) explains about GA (p=0.81). Then, again, the author mentioned about GA at the first line of page 7, such as “While the mean GA and preterm birth rate were comparable”. Better to erase either one.

(6) At the forth line in page 7, the author(s) mentioned “with higher rates of hypoglycemia, hypocalcemia”. Yet, Table 1 does not show the rates of these variables. Please add these in Table 1, if the author(s) use these variables for the further statistical analyses.

(7) In “RESULTS-Neighborhood deprivation and perinatal outcomes” in page 7, the author(s) mentioned “… and to be prenatally exposed to maternal smoking”, which is different from the results shown in Table 2. Table 2 shows that infants born to mothers living in Q2 and Q3 of NDI are more likely to expose to maternal smoking (1.9%), than Q4 (1.4%) and Q1 (0.8%). Please revise the sentence.

(8) Again, in the same section as above (5), the author(s) repeatedly explain about the ratio of LGA. In the 2-3 lines of this section, the author(s) said “The proportion of LGA varies by NDI levels, reaching 12% in NDI level 4”. Then, in the 3-4th lines from the bottom of this section, the author(s) repeatedly explain “…, while we observe a higher rate of LGA with the NDI (12.3% in NDI level 4 vs 9.9% in NDI level 1, p<0.0001)”. Please drop the first part.

(9) As mentioned in my major comments, I do not see any reasons why the author(s) show “Univariate analysis” in Table 3, which is not necessary. Instead, the author(s) should show the results of supplemental table 2 & supplemental data table 3.

Reviewer #2: This manuscript examined associations between neighbourhood-level socioeconomic status and large-for-gestational age (LGA) risk in a sample of 43,309 births between 2013 and 2016 in Marseilles, France. Independent of other variables, lower neighbourhood socioeconomic status was associated with increased risk for LGA status, and this trend was pronounced for older women. This is a well written and interesting article. Specific comments are below.

First, exploring interactions between neighbourhood SES and other variables, and specifically with age, were not developed or justified in the Introduction. Theoretical background justifying the examination of this interaction, and preparing the reader for presentation of interaction findings in the Results, is needed.

Related, the authors note in the Results that “as expected, maternal smoking was a protective factor.” For the reader, this finding is not expected at all because the authors provide no literature discussing the effect of smoking on LGA risk. And smoking during pregnancy is not usually considered a good or “protective” factor, which is what this sentence implies. Please consider revising and providing more context for this finding.

Second, did the authors consider excluding children with congenital defects, which could have affected pregnancy and infant size at birth?

Third, a key and interesting finding is that the effect of neighbourhood socioeconomic status on LGA status is larger for older women compared to younger. The authors also report, however, that older women in the more- and less-deprived neighbourhoods seem to differ in significant ways. Could the authors explicitly characterize the older women in more-deprived neighbourhoods, provide context for those differences, and tie that into clinical implications for these findings?

Fourth, the authors say in the Discussion that “neighbourhood SES may thus be a good proxy for capturing other mechanisms associated with LGA.” The proxies listed are very heterogenous, and as the authors note not well captured in this data set. I would advise caution in making such a statement about using neighbourhood socioeconomic status as a “proxy” for everything from poor health behaviours, endocrine disruptors and race/ethnicity. Please revise.

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Reviewer #1: No

Reviewer #2: No

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Attachment

Submitted filename: Comments on PONE-D-19-26817.pdf

Decision Letter 1

Diane Farrar

14 Apr 2020

PONE-D-19-26817R1

The role of neighborhood socioeconomic status in large for gestational age

PLOS ONE

Dear Prof Boyer,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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Firstly you say in the Discussion that “neighbourhood SES may thus be a good proxy for capturing other mechanisms associated with LGA.” The proxies listed are very heterogenous, and as noted not well captured in this data set. I would advise caution in making such a statement about using neighbourhood socioeconomic status as a “proxy” for everything from poor health behaviours, endocrine disruptors and race/ethnicity. Please revise accordingly.”

Secondly I am concerned about the following paragraph: “In line with the previous point, LGA prevalence in deprived neighborhoods was not expected to be so high (i.e., 25% higher than in less deprived neighborhoods) due to the universal health coverage that is supposed to reduce health care inequalities[27]. One explanation is that access to care is a multidimensional concept consisting of both financial and nonfinancial dimensions [43]. Our findings may suggest that nonfinancial barriers may substantially limit access to care despite universal health coverage in this study. In accordance with this hypothesis, a previous French study performed in Paris reported insufficient healthcare follow-up in women with low socioeconomic status[44]. Future studies should better explore nonfinancial barriers to care, and targeted interventions should be proposed for reducing nonfinancial barriers in these deprived neighborhoods.” This paragraph makes it seem as if you are unfamiliar with the vast literature on the social determinants of health and the literature on health care disparities, which identify myriad factors, in addition to health coverage, that contribute to health and health care disparities. Also, LGA prevalence is a health disparity, not a health care disparity, so please consider the contributors to both and revise.

I have a few minor suggestions:

1.            You state “To our knowledge, only a few studies in the United States and Canada have addressed this issue with conflicting results [28-30].” It would be helpful to know what those conflicting results were, and if possible, why the authors believe these studies have produced conflicting results and, in the discussion, how this study addresses these conflicting results.

2.            You should spell out abbreviations the first time they are used (e.g., GDM, AGA). They should consider using fewer abbreviations to improve readability, especially since this is geared to a multi-disciplinary audience who are not likely to be familiar with these abbreviations.

3.            I recommend another round of editing, as there are some grammatical errors.”

==============================

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Academic Editor

PLOS ONE

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: (No Response)

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: I appreciate the author(s) to accept all my suggestions. I do not have any further comments on this article.

Reviewer #2: Thank you for the opportunity to review this manuscript again. The authors have addressed all my comments.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

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Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files to be viewed.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email us at figures@plos.org. Please note that Supporting Information files do not need this step.

Decision Letter 2

Diane Farrar

6 May 2020

The role of neighborhood socioeconomic status in large for gestational age

PONE-D-19-26817R2

Dear Dr. Boyer,

We are pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it complies with all outstanding technical requirements.

Within one week, you will receive an e-mail containing information on the amendments required prior to publication. When all required modifications have been addressed, you will receive a formal acceptance letter and your manuscript will proceed to our production department and be scheduled for publication.

Shortly after the formal acceptance letter is sent, an invoice for payment will follow. To ensure an efficient production and billing process, please log into Editorial Manager at https://www.editorialmanager.com/pone/, click the "Update My Information" link at the top of the page, and update your user information. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, you must inform our press team as soon as possible and no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

With kind regards,

Diane Farrar

Academic Editor

PLOS ONE

Acceptance letter

Diane Farrar

18 May 2020

PONE-D-19-26817R2

The role of neighbourhood socioeconomic status in large for gestational age

Dear Dr. Boyer:

I am pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please notify them about your upcoming paper at this point, to enable them to help maximize its impact. If they will be preparing press materials for this manuscript, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

For any other questions or concerns, please email plosone@plos.org.

Thank you for submitting your work to PLOS ONE.

With kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Diane Farrar

Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. Comparison of the neighbourhood deprivation index used in the study with related variables from the 2015 French National Census data.

    (DOCX)

    Attachment

    Submitted filename: Comments on PONE-D-19-26817.pdf

    Attachment

    Submitted filename: Response to reviewers_Letter_17032020.docx

    Attachment

    Submitted filename: Response to reviewers_Letter_02052020.docx

    Data Availability Statement

    Data cannot be shared publicly because of this data are issued from French national medicoadministrative database. Data are available from the ATIH Institutional Data Access (contact via https://www.atih.sante.fr/) for researchers who meet the criteria for access to confidential data. The data underlying the results presented in the study are available from ATIH (https://www.atih.sante.fr/).


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