Skip to content

Advertisement

BMC Pregnancy and Childbirth

  • Research article
  • Open Access
  • Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Mother’s education and the risk of several neonatal outcomes: an evidence from an Italian population-based study

BMC Pregnancy and ChildbirthBMC series – open, inclusive and trusted201717:221

https://doi.org/10.1186/s12884-017-1418-1

©  The Author(s). 2017

Received: 28 March 2017

Accepted: 5 July 2017

Published: 12 July 2017

Open Peer Review reports

Abstract

Background

Maternal socioeconomic disparities strongly affect child health, particularly in low and middle income countries. We assessed whether neonatal outcomes varied by maternal education in a setting where healthcare system provides universal coverage of health services to all women, irrespective of their socioeconomic status.

Methods

A population-based study was performed on 383,103 singleton live births occurring from 2005 to 2010 in Lombardy, an Italian region with approximately 10 million inhabitants. The association between maternal education, birthplace and selected neonatal outcomes (preterm birth, low birth weight, small-for-gestational age, low 5-min Apgar score, severe congenital anomalies, cerebral distress and respiratory distress) was estimated by fitting logistic regression models. Model adjustments were applied for sociodemographic, reproductive and medical maternal traits.

Results

Compared with low-level educated mothers, those with high education had reduced odds of preterm birth (Odds Ratio; OR = 0.81, 95% CI 0.77–0.85), low birth weight (OR = 0.78, 95% CI 0.70–0.81), small for gestational age (OR = 0.82, 95% CI 0.79–0.85), and respiratory distress (OR = 0.84, 95% CI 0.80–0.88).

Mothers born in a foreign country had higher odds of preterm birth (OR = 1.16, 95% CI 1.11–1.20), low Apgar score (OR = 1.18, 95% CI 1.07–1.30) and respiratory distress (OR = 1.19, 95% CI 1.15–1.24) than Italian-born mothers. The influence of maternal education on neonatal outcomes was confirmed among both, Italian-born and foreign-born mothers.

Conclusions

Low levels of education and maternal birthplace are important factors associated with adverse neonatal outcomes in Italy. Future studies are encouraged to investigate factors mediating the effects of socioeconomic inequality for identifying the main target groups for interventions.

Keywords

Socioeconomic inequalityMaternal educationMaternal birthplaceAdverse neonatal outcomesPregnancy and birth

Background

Maternal socioeconomic status (SES) strongly affects child health [1–6], likely attributed to delayed prenatal care, preterm delivery and adverse birth outcomes [7–14]. Different SES measures capture unique aspects and pathways of socioeconomic disparities that can relate differently to child health. For example, maternal education reflects life-course SES [15], including parents’ SES during childhood and adolescence, access to higher education, work opportunities, and income during adulthood [16]. According to a systematic review of studies in industrialized countries, maternal education, rather than maternal income, has been found to correlate with birth outcomes [17].

Differences in the ability to access good-quality obstetric services and neonatal care may be due to differences in maternal socioeconomic status [2]. The Italian National Health Service (NHS) provides universal coverage for many areas of healthcare, including obstetric, neonatal and related health care services to women, regardless of their SES [18]. Neonatal outcomes are expected to be only partially affected by socioeconomic inequalities in health systems with universal access to essential health services [2].

We performed a large population-based study aimed to measure the relationship between maternal education and several neonatal outcomes (i.e., preterm birth, low birth weight, small for gestational age, 5-min Apgar less than 7, severe congenital anomalies, signs of cerebral distress and distress of respiratory functions) in the Italian region of Lombardy. Our analysis took into consideration other maternal features (i.e., maternal birthplace, sociodemographic factors, reproductive history, and medical conditions), as well as investigating the impact of all maternal traits.

Methods

Setting

Data obtained for this study were retrieved from the healthcare utilization (HCU) databases of Lombardy, a region of Italy which accounts for approximately 16% (~ 10 million) of the national population. In Italy, the entire population is covered by the NHS, which in Lombardy has been active since 1997 with an automated system of databases to collect a variety of HCU information. For the purpose of the current study, the following databases were considered: (i) the archive of beneficiaries of the Regional Health Service (RHS), i.e., the entire resident population, reporting demographic and administrative data (e.g., municipality, date of birth and date of start and end of being RHS beneficiary), (ii) the database on diagnosis at discharge from public or private hospitals of Italy (diagnoses classified according to the International Code of Disease, 9th Revision, ICD-9); and (iii) the database reporting Certificates of Delivery Assistance (CeDAP) including information self-reported by the mother relating to her socioeconomic traits in the period recent to her current pregnancy, other than medical information relating to pregnancy, childbirth, and child presentation at delivery. In general, information was collected and directly added to the specific database when the specific service was provided, for example, when an individual was recorded for being a RHS beneficiary, a patient discharged from hospital, or a woman who gave birth.

As each single record for the aforementioned databases utilises an univocal identification code, the record linkage between databases was allowed. In order to preserve privacy, however, each identification code was automatically converted into an anonymous code and the inverse process was prevented by the deletion of the conversion table. For the current application, a deterministic procedure of record linkage between the above listed databases was performed so as to select the study cohort and collect data on maternal traits and newborn outcomes.

Cohort selection

The 428,715 singleton live births that occurred in Lombardy from 2005 to 2010 were selected from the CeDAP database, provided that identification codes of both mother and newborn were reported. We sequentially excluded (Fig. 1) (i) 10,961 newborns (2.6%) because of a missing identification code (CeDAP database); (ii) 26,284 records (6.3%) because the mother was resident outside the Lombardy region (RHS beneficiaries archive); (iii) 6696 records (1.7%) because the reported hospital admission ICD-9 code of mother and/or newborn was different from that of the delivery and/or birth (hospital discharge database); and (iv) 1671 records (0.4%) because the mother was younger than 15 years or older than 55 years of age at delivery (RHS beneficiaries archive). The final study cohort included 383,103 mother-newborn couples.
Figure 1
Fig. 1

Flow-chart of inclusion and exclusion criteria

Collection of data on maternal traits

Information on maternal traits at the time of delivery was obtained from the CeDAP database and included age at delivery (≤25, 25–34 and ≥35 years), sociodemographic factors and reproductive history. Sociodemographic factors included (i) education, measured according to the length of formal education completed and categorized as ≤8 years (low), from 9 to 13 years (intermediate), and ≥14 years (high); (ii) birthplace, categorized as Italian-born and foreign-born, (iii) employment, categorized as employed and unemployed (the latter including women without a job, housewives and students); and (iv) marital status, categorized as married and unmarried. Reproductive history included (i) parity categorized as null parity and multi parity; and (ii) previous spontaneous miscarriages (yes/no). In addition, maternal medical conditions were identified from inpatient diagnoses (hospital discharge database) within the 2 years prior to date of delivery and included hypertension, dyslipidaemia, diabetes and preeclampsia. Additional file 1: Table S1 presents the ICD-9 codes used for identifying maternal medical conditions.

Identification of newborn outcomes

Newborn outcomes appearing at presentation and within 2 years after birth were respectively identified from the CeDAP and the hospital discharge database. At presentation, we considered preterm birth (less than 37 weeks’ gestation [19]), low birth weight (below 2500 g [20]), small for gestational age (birth-weight less than 10th percentile for infants from 22 to 43 weeks [21, 22]), and low 5-min Apgar score (5-min Apgar <7 [23]).

From the hospital discharge database the following three categories of neonatal outcomes were considered: (i) severe congenital anomalies, defined according to the EUROCAT classification (www.eurocat-network.eu) and included anomalies of the nervous, respiratory, digestive, urinary and genital systems, and defects of eye, ear, face and neck, heart, abdominal wall and limb; (ii) cerebral distress, including convulsion, other and unspecified cerebral irritability in newborn, cerebral depression, coma, and other abnormal cerebral signs; and (iii) distress of respiratory function, including intrauterine hypoxia, birth asphyxia and other respiratory conditions of foetus and newborn. Additional file 1: Table S2 summarises ICD-9 codes used for identifying these categories of newborn outcomes. Primary or secondary diagnosis were considered for identifying the onset of outcome.

Statistical analysis

The frequency of a given neonatal outcome within strata of the considered maternal traits was evaluated by testing for heterogeneity between strata (of maternal birthplace, employment, marital status, reproductive history and medical conditions) or trend over strata (of educational status and age at delivery) respectively according to chi-square test, or its version for trend.

A logistic regression model was fitted to estimate the odds ratio (OR), and its 95% confidence interval (CI), of a given neonatal outcome in relation to categories of maternal education and birthplace. The influence of maternal education on neonatal outcomes was evaluated by considering the entire sample of mother-newborn couples in addition to stratifying data according to maternal birthplace. Linear trend in ORs for different levels of education was tested by using the contrast statement implemented in SAS [24]. Model adjustments were made for the above reported sociodemographic, reproductive and medical maternal traits.

The following two expedients were used for taking into account the nature of our data. First, because of the potential correlation of women contributing to more than one birth during the considered period, the models were fitted using Generalized Estimating Equations (GEE) for correlated observations with a logit link [25]. Two, because data were missing for some women (ranging missing values from 1% for previous miscarriages to 13% for marital status), 100 multiple imputations were applied by using the fully conditional specification (FCS) method implemented in SAS [26, 27].

All analyses were performed using the Statistical Analysis System Software (version 9.4; SAS Institute, Cary, NC, USA). Statistical significance was set at the 0.05 level. All p-values were two-sided.

Results

Just over 1 in 20 newborns were found to be affected from low birth weight (prevalence 5.1%), respiratory distress (5.1%), preterm birth (5.3%), small for gestational age (7.8%) and severe congenital anomalies (5.0%). Lower prevalence was observed for low Apgar score (0.8%) and cerebral distress (0.3%).

It also emerged that as educational level increases, the frequency of several outcomes (i.e., preterm birth, low birth weight, small for gestational age, cerebral distress and respiratory distress) decreases proportionally (Table 1). Other maternal traits (e.g., older age, foreign-born, unmarried and unemployment status, null parity, previous miscarriages and suffering from medical conditions) were significantly associated with several neonatal outcomes.
Table 1

Frequency of neonatal outcomes according to selected maternal traits. Italy, Lombardy Region, 2005–2010

 

All women

Preterm birth

Low birth weight

Small for Gestational Age

Low Apgar score

Congenital Anomalies

Cerebral distress

Respiratory distress

(N = 383,103)

(N = 20,294)

(N = 19,588)

(N = 29,800)

(N = 3180)

(N = 18,997)

(N = 996)

(N = 15,539)

Maternal trait

%

%

%

%

%

%

%

%

Educationa

 Low

121,910

5.8%

5.6%

8.1%

0.9%

4.9%

0.3%

5.4%

 Intermediate

173,926

5.2%

5.1%

7.7%

0.8%

4.8%

0.3%

5.0%

 High

87,267

4.7%

4.6%

7.3%

0.7%

5.2%

0.2%

4.7%

   p-valueb

 

<0.0001

<0.0001

<0.0001

0.0466

0.0044

0.0444

<.0001

Maternal birthplace

 Italian-born

288,093

5.2%

4.9%

8.1%

0.8%

4.9%

0.2%

4.9%

 Foreign-born

95,010

5.6%

5.2%

6.8%

0.9%

4.9%

0.3%

5.6%

   p-valueb

 

<0.0001

0.0009

<0.0001

0.0001

0.1617

0.0200

<.0001

Age at delivery

 ≤ 25 years

49,803

4.9%

4.9%

8.4%

0.9%

4.8%

0.2%

5.2%

 26–35 years

244,037

5.0%

4.9%

7.8%

0.8%

4.8%

0.3%

4.9%

 ≥ 35 years

89,263

6.2%

5.8%

7.5%

0.9%

5.5%

0.2%

5.4%

   p-valueb

 

<0.0001

<0.0001

0.1739

0.0009

<.0001

0.3189

<.0001

Marital status

 Married

294,606

5.2%

4.9%

7.4%

0.8%

4.9%

0.3%

4.9%

 Unmarried

88,497

5.8%

5.9%

9.1%

0.9%

5.1%

0.3%

5.6%

   p-valueb

 

<0.0001

<0.0001

<0.0001

0.0003

0.0245

0.7656

<.0001

Employment

 Employed

270,088

5.2%

5.1%

7.9%

0.8%

5.0%

0.2%

5.0%

 Unemployed

113,015

5.4%

5.1%

7.5%

0.9%

4.8%

0.3%

5.2%

   p-valueb

 

0.0170

0.3370

0.0003

0.1516

0.0691

0.0454

0.0068

Parity

 Nulliparous

211,090

5.7%

6.0%

9.7%

0.9%

5.4%

0.3%

5.9%

 Multiparous

172,013

4.8%

3.9%

5.5%

0.7%

4.5%

0.2%

4.0%

   p-valueb

 

<0.0001

<0.0001

<0.0001

<0.0001

<0.0001

<.0001

<.0001

Previous spontaneous abortions

 No

320,274

5.1%

5.0%

7.9%

0.8%

4.9%

0.3%

5.1%

 Yes

62,829

6.1%

5.6%

7.0%

0.9%

5.2%

0.3%

4.9%

   p-valueb

 

<0.0001

<0.0001

<0.0001

0.0689

0.0008

0.5183

0.0223

Diabetes

 No

371,227

5.2%

5.1%

7.8%

0.8%

4.9%

0.3%

5.0%

 Yes

11,915

9.3%

5.8%

6.1%

1.4%

6.5%

0.4%

6.8%

   p-valueb

 

<0.0001

0.0007

<0.0001

<0.0001

<0.0001

0.0019

<.0001

Hypertension

 No

370,077

5.0%

4.8%

7.6%

0.8%

4.9%

0.3%

5.0%

 Yes

13,026

12.9%

14.6%

12.8%

1.4%

5.9%

0.3%

6.9%

   p-valueb

 

<0.0001

<0.0001

<0.0001

<0.0001

<0.0001

0.7711

<.0001

Dyslipidaemia

 No

382,202

5.3%

5.1%

7.8%

0.8%

4.9%

0.3%

5.1%

 Yes

901

9.7%

6.9%

6.7%

0.9%

5.5%

0.5%

6.7%

   p-valueb

 

<0.0001

0.0158

0.2091

0.8481

0.4135

0.0817

0.0332

Preeclampsia

 No

373,909

4.8%

4.6%

7.5%

0.8%

4.9%

0.3%

4.9%

 Yes

9194

26.0%

27.5%

17.7%

2.2%

7.9%

0.4%

11.5%

   p-valueb

 

<0.0001

<0.0001

<0.0001

<0.0001

<0.0001

0.0158

<.0001

aYears of formal education completed categorized as ≤8 years (low), from 9 to 13 years (intermediate), and ≥14 years (high)

bAccording to chi-square test or its version for the trend (education and age at delivery)

The relationship between maternal education and birthplace and selected neonatal outcomes is summarised in Table 2. With the exception of severe congenital anomalies, significant trends showing a decrease in adjusted ORs as maternal education increases were observed for all of the considered neonatal outcomes, including those recorded at presentation (preterm birth, low birth weight, small for gestational age), as well as those recorded within the first 2 years of life (cerebral distress and respiratory distress). Compared to Italian-born mothers, foreign-born mothers had a higher odds of preterm birth, low Apgar score and respiratory distress, while they had lower odds of being small for gestational age. The influence of maternal education on neonatal outcome was confirmed in both Italian-born and foreign-born mothers (Table 3).
Table 2

Relationship between maternal education and birthplace and selected neonatal outcomes. Italy, Lombardy Region, 2005–2010

 

Preterm birth

Low birth weight

Small for Gestational Age

Low Apgar score

Severe congenital Anomalies

Cerebral distress

Respiratory distress

OR b (95% CI)

OR b (95% CI)

OR b (95% CI)

OR b (95% CI)

OR b (95% CI)

OR b (95% CI)

OR b (95% CI)

Education a

 Low

1.00 (reference)

1.00 (reference)

1.00 (reference)

1.00 (reference)

1.00 (reference)

1.00 (reference)

1.00 (reference)

 Intermediate

0.90 (0.87–0.94)

0.87 (0.84–0.90)

0.88 (0.86–0.91)

0.98 (0.90–1.07)

0.94 (0.91–0.98)

1.00 (0.86–1.16)

0.91 (0.87–0.94)

 High

0.81 (0.77–0.85)

0.78 (0.74–0.81)

0.82 (0.79–0.85)

0.92 (0.83–1.03)

1.02 (0.97–1.06)

0.84 (0.69–1.02)

0.84 (0.80–0.88)

 p-trend b

<0.0001

<0.0001

<0.0001

0.0164

0.1155

0.4745

<0.0001

Birthplace

 Italian-born

1.00 (reference)

1.00 (reference)

1.00 (reference)

1.00 (reference)

1.00 (reference)

1.00 (reference)

1.00 (reference)

 Foreign-born

1.16 (1.11–1.20)

0.98 (0.94–1.03)

0.82 (0.79–0.85)

1.18 (1.07–1.30)

1.01 (0.97–1.06)

1.17 (0.99–1.39)

1.19 (1.15–1.24)

a Years of formal education completed categorized as ≤8 years (low), from 9 to 13 years (intermediate), and ≥14 years (high)

b Odds ratios (and 95% confidence interval) were derived from logistic regression. Full multivariable models for each outcome included as covariates maternal traits (i.e., age at delivery, marital status, employment, parity, previous spontaneous miscarriages, diabetes, hypertension, dyslipidaemia and preeclampsia) categorized as in Table 1

Table 3

Relationship between maternal education and selected neonatal outcomes according to maternal birthplace. Italy, Lombardy Region, 2005–2010

 

Preterm birth

Low birth weight

Small for Gestational Age

Low Apgar score

Severe congenital Anomalies

Cerebral distress

Respiratory distress

OR b (95% CI)

OR b (95% CI)

OR b (95% CI)

OR b (95% CI)

OR b (95% CI)

OR b (95% CI)

OR b (95% CI)

 

Italian-born mothers

Education a

 Low

1.00 (reference)

1.00 (reference)

1.00 (reference)

1.00 (reference)

1.00 (reference)

1.00 (reference)

1.00 (reference)

 Intermediate

0.88 (0.85–0.92)

0.86 (0.82–0.89)

0.88 (0.85–0.92)

0.95 (0.84–1.08)

0.97 (0.93–1.01)

0.99 (0.83–1.19)

0.90 (0.85–0.94)

 High

0.79 (0.76 to 0.84)

0.77 (0.73 to 0.81)

0.82 (0.79 to 0.85)

0.98 (0.88–1.10)

1.06 (0.99–1.12)

0.85 (0.68–1.08)

0.84 (0.80–0.88)

 p-trend b

<0.0001

<0.0001

<0.0001

0.3129

0.0997

0.6704

<0.0001

 

Foreign-born mothers

Education a

 Low

1.00 (reference)

1.00 (reference)

1.00 (reference)

1.00 (reference)

1.00 (reference)

1.00 (reference)

1.00 (reference)

 Intermediate

0.94 (0.88–1.01)

0.90 (0.84–0.97)

0.88 (0.83–0.94)

0.97 (0.83–1.14)

0.88 (0.82–0.94)

1.00 (0.77–1.31)

0.92 (0.86–0.99)

 High

0.84 (0.77–0.92)

0.78 (0.71–0.87)

0.84 (0.77–0.92)

0.81 (0.61–1.07)

0.90 (0.82–0.99)

0.78 (0.52–1.16)

0.81 (0.73–0.90)

 p-trend b

<0.0001

<0.0001

<0.0001

0.2615

<0.0001

0.9614

<0.0001

a Years of formal education completed categorized as ≤8 years (low), from 9 to 13 years (intermediate), and ≥14 years (high)

b Odds ratios (and 95% confidence interval) were derived from logistic regression. Full multivariable models for each outcome included as covariates maternal traits (i.e., age at delivery, marital status, employment, parity, previous spontaneous miscarriages, diabetes, hypertension, dyslipidaemia and preeclampsia) categorized as in Table 1

Discussion

The main findings from the present study show that even in a country with universal access to essential health care services such as Italy, mothers with higher levels of education were at lower risk of several neonatal adverse outcomes. These differences cannot be underestimated, since compared to mothers with lower levels of education, those with high levels of education had 19, 22, 18, and 16% decreased risk of preterm birth, low birth weight, small for gestational age and respiratory distress, respectively. Corroborating our findings, a recent meta-analysis conducted across 12 European countries revealed a 48% risk excess of preterm births associated with low maternal education [28].

It was reported that among mother social aspects, education is considered the most powerful determinant of health [29]. Other mother’s traits influencing birth health, however, deserve to be mentioned. One, our study confirms previous observations that in Western countries a high proportion of births are to migrant women [30]. Migrant status has been associated with several adverse neonatal outcomes in some [31–36], but not all [36–41] studies, possibly because of differences in access to healthcare services [32, 42, 43], and integration policies of the host countries [44]. Our study shows that, compared to Italian-born mothers, foreign-born ones were at higher risk for preterm birth, low Apgar score and respiratory distress, while they had lower risk of being small for gestational age. Two, our study confirms that advanced maternal age [44–46], nulliparous [47], and unmarried status [48, 49] are risk factors for some adverse perinatal outcomes. Three, in the current study, unemployed mothers were at a higher risk of some adverse neonatal outcomes, likely because the condition might be a proxy of social inequality uncaptured by education and birthplace. This finding is consistent with studies showing the influence of employment status on preterm birth, small for gestational age and other neonatal outcomes [50, 51]. Finally, we confirmed previous evidence that diabetes, hypertension and to a greater extent pre-eclampsia and drug therapies for managing these concomitant diseases, are leading causes of adverse neonatal outcomes [52–57].

Our study has a number of potential limitations. First, the exclusion of mother-newborn pairs lacking identification codes could mainly affect less healthy women. Second, we did not collect information on income, a factor recognised to be associated with perinatal outcomes [1–4, 6]. More importantly, we did not have data on the country of origin of maternal birthplace. This may have resulted in residual confounding due to the unknown gradient of the effect of socioeconomic status. We are confident that the exclusion of this information did not influence the results observed since we also included information on maternal occupation. Third, privacy concerns did not allow of assessing the validity of information recorded in the Certificates of Delivery Assistance, as well as of diagnostic data from hospital charts. Finally, the lack of data on important factors, such as smoking, pre-pregnancy weight and gestational weight gain, may further contribute to some unavoidable source of systematic uncertainty.

Conclusion

Notwithstanding these limitations, our study shows that, in a setting where healthcare system provides essential health services to all women, irrespective of their socioeconomic status, mother’s education and other socioeconomic factors are strongly associated with some adverse perinatal outcomes, including preterm birth, low Apgar score, cerebral distress, respiratory distress, and SGA. These findings merit attention from a public health perspective. Future studies are encouraged to investigate factors mediating the effects of socioeconomic inequality on birth outcomes for identifying the main target groups for interventions.

Abbreviations

CeDAP: 

Certificates of Delivery Assistance

CI: 

Confidence interval

HCU: 

HealthCare Utilization

ICD-9: 

International code of disease, ninth revision

NHS: 

National Health Service

OR: 

Odds ratio

RHS: 

Regional Health Service

SES: 

Socioeconomic status

Declarations

Acknowledgements

The authors wish to acknowledge the Pedianet Project for the support provided to the Laboratory of Healthcare Research and Pharmacoepidemiology with reference to the research carried out in paediatrics.

Funding

This study was funded by grants from the Italian Ministry of the Education, University and Research (‘Fondo d’Ateneo per la Ricerca’ portion, year 2015). The funder had not any role in the collection, analysis, interpretation of the data or access to the raw data. The corresponding author had full access to all the data and the final responsibility to submit for publication.

Availability of data and materials

The data that support the findings of this study are available from the Operative Unit of Territorial Health Services of Lombardy but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the Operative Unit of Territorial Health Services of Lombardy.

Authors’ contributions

All authors (AC, MF, MMC, LM and GC) have made substantial contributions to conception and design, revision of the literature searches, assessement of the studies for quality, data extraction, analysis and interpretation of the data as well as drafting and revising the manuscript. AC and GC undertook the literature searches, GC coordinated the systematic review process. AC, MF, MMC, LM, and GC read and approved the final version of the manuscript.

Ethics approval and consent to participate

According to the rules from the Italian Medicines Agency (available at: http://www.agenziafarmaco.gov.it/sites/default/files/det_20marzo2008.pdf) retrospective studies without direct contact with patients do not need a written consent to process personal data when they are used for research aims.

Consent for publication

Not applicable.

Competing interests

GC received research support from the European Community (EC), the Italian Agency of Drug (AIFA), and the Italian Ministry for University and Research (MIUR). He took part to a variety of projects that were funded by pharmaceutical companies (i.e., Novartis, GSK, Roche, AMGEN and BMS). He also received honoraria as member of Advisory Board from Roche.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Statistics and Quantitative Methods, Division of Biostatistics, Epidemiology and Public Health, Laboratory of Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy
(2)
Operative Unit of Territorial Health Services, Milan, Italy

References

  1. Parker J, Schoendorf K, Kiely J. Associations between measures of socioeconomic status and low birth weight, small for gestational age, and premature delivery in the United States. Ann Epidemiol. 1994;4:271–8.View ArticlePubMedGoogle Scholar
  2. Joseph K, Liston R, Dodds L, Dahlgren L, Allen A. Socioeconomic status and perinatal outcomes in a setting with universal access to essential health care services. CMAJ. 2007;177:583–90.View ArticlePubMedPubMed CentralGoogle Scholar
  3. Kramer MS, Seguin L, Lydon J, Goulet L. Socio-economic disparities in pregnancy outcome: why do the poor fare so poorly? Paediatr Perinat Epidemiol. 2000;14:194–210.View ArticlePubMedGoogle Scholar
  4. Auger N, Giraud J, Daniel M. The joint influence of area income, income inequality, and immigrant density on adverse birth outcomes: a population-based study. BMC Public Health. 2009;9:237.View ArticlePubMedPubMed CentralGoogle Scholar
  5. Morgen CS, Bjork C, Andersen PK. Socioeconomic position and the risk of preterm birth a study within the Danish National Birth Cohort. Int J Epidemiol. 2008;37:1109–20.View ArticlePubMedGoogle Scholar
  6. Mortensen HL, Helweg-Larsen K, Andersen AMN. Socioeconomic differences in perinatal health and disease. Scand J Public Health. 2011;39(Suppl 7):110–4.View ArticlePubMedGoogle Scholar
  7. Martens PJ, Derksen S, Gupta S. Predictors of hospital readmission of Manitoba newborns within six weeks postbirth discharge: a population-based study. Pediatrics. 2004;114:708–13.View ArticlePubMedGoogle Scholar
  8. Joseph KS, Nette F, Scott H, Vincer MJ. Prenatal corticosteroid prophylaxis for women delivering at late preterm gestation. Pediatrics. 2009;124:e835–43.View ArticlePubMedGoogle Scholar
  9. Balchin I, Steer PJ. Race, prematurity and immaturity. Early Hum Dev. 2007;83:749–54.View ArticlePubMedGoogle Scholar
  10. DuPlessis HM, Bell R, Richards T. Adolescent pregnancy: understanding the impact of age and race on outcomes. J Adolesc Health. 1997;20:187–97.View ArticlePubMedGoogle Scholar
  11. Shapiro-Mendoza CK, Tomashek KM, Kotelchuck M, Barfield W, Nannini A, Weiss J, et al. Effect of late-preterm birth and maternal medical conditions on newborn morbidity risk. Pediatrics. 2008;121:e223.View ArticlePubMedGoogle Scholar
  12. Dominguez TP. Race, racism, and racial disparities in adverse birth outcomes. Clin Obstet Gynecol. 2008;51:360–70.View ArticlePubMedGoogle Scholar
  13. Heaman M, Gupton A, Moffatt M. Prevalence and predictors of inadequate prenatal care: a comparison of aboriginal and non-aboriginal women in Manitoba. J Obstet Gynaecol Can. 2005;27:237–46.View ArticlePubMedGoogle Scholar
  14. Feijen-de Jong E, Jansen D, Baarveld F, van der Schans C, Schellevis F, Reijneveld S. Determinants of late and/or inadequate use of prenatal healthcare in high-income countries: a systematic review. Eur J Pub Health. 2012;22:904–13.View ArticleGoogle Scholar
  15. Shavers V. Measurement of socioeconomic status in health disparities research. J Natl Med Assoc. 2007;99:1013–23.PubMedPubMed CentralGoogle Scholar
  16. Daoud N, O’Campo P, Minh A, Urquia ML, Dzakpasu S, Heaman M, et al. Patterns of social inequalities across pregnancy and birth outcomes: a comparison of individual and neighborhood socioeconomic measures. BMC Pregnancy Childbirth. 2014;14:e393.View ArticleGoogle Scholar
  17. Blumenshine P, Egerter S, Barclay CJ, Cubbin C, Braveman PA. Socioeconomic disparities in adverse birth outcomes: a systematic review. Am J Prev Med. 2010;39:263–72.View ArticlePubMedGoogle Scholar
  18. Dotta A, Portanova A, Bianchi N, Ciofi Degli Atti M, Zanini R, Raponi M. Accreditation of birth centres: advantages for newborns. J Matern Fetal Neonatal Med. 2013;26:417–8.View ArticlePubMedGoogle Scholar
  19. Lawn JE, Gravett MG, Nunes TM, Rubens CE, Stanton C, GAPPS Review Group. Global report on preterm birth and stillbirth (1 of 7): definitions, description of the burden and opportunities to improve data. BMC Pregnancy Childbirth. 2010;10(Suppl 1):S1.View ArticlePubMedPubMed CentralGoogle Scholar
  20. Valero de Bernabé J, Soriano T, Albaladejo R, Juarranz M, Calle ME, Martínez D, et al. Risk factor for low birth weight: a review. Eur J Obstet Gynecol. 2004;116:3–15.View ArticleGoogle Scholar
  21. Villar J, et al. International standards for fetal growth based on serial ultrasound measurements: the fetal growth longitudinal study of the INTERGROWTH-21st project. Lancet. 2014;384(9946):869–79.View ArticlePubMedGoogle Scholar
  22. Villa J, et al. INTERGROWTH-21st consortium. INTERGROWTH-21st very preterm size at birth reference charts. Lancet. 2016;387(10021):844–5.View ArticleGoogle Scholar
  23. Casey BM, McIntire DD, Leveno KJ. The continuing value of the Apgar score for the assessment of newborn infants. N Engl J Med. 2001;344:467–71.View ArticlePubMedGoogle Scholar
  24. SAS Institute Inc. SAS note 351, “CONTRAST and ESTIMATE statements made easy: the LSMESTIMATE statement”. Cary: SAS Institute Inc.; 2011. Available at https://support.sas.com/resources/papers/proceedings11/351-2011.pdf.Google Scholar
  25. Zeger SL, Liang KY. Longitudinal data analysis for discrete and continuous outcomes. Biometrics. 1986;42(1):121-30. PubMed PMID: 3719049.Google Scholar
  26. Magnus MC, Stigum H, Håberg SE, Nafstad P, London SJ, Nystad W. Peak weight and height velocity to age 36 months and asthma development: the Norwegian mother and child cohort study. PLoS One. 2015;10(1):e0116362.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Verret-Chalifour J, Giguère Y, Forest JC, Croteau J, Zhang P, Marc I. Breastfeeding initiation: impact of obesity in a large Canadian perinatal cohort study. PLoS One. 2015;10(2):e0117512.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Ruiz M, Goldblatt P, Morrison J, Kukla L, Švancara J, Riitta-Järvelin M, et al. Mother's education and the risk of preterm and small for gestational age birth: a DRIVERS meta-analysis of 12 European cohorts. J Epidemiol Community Health. 2015;69(9):826–33.View ArticlePubMedPubMed CentralGoogle Scholar
  29. Luo ZC, Wilkins R, Kramer MS, Fetal and Infant Health Study Group of the Canadian Perinatal Surveillance System. Effect of neighbourhood income and maternal education on birth outcomes: a population-based study. CMAJ. 2006;174(10):1415–20.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Bona G, Zaffaroni M, Cataldo F, et al. Infants of immigrant parents in Italy. A national multicentre case control study. Panminerva Med. 2001;43:155–9.PubMedGoogle Scholar
  31. Sosta E, Tomasoni LR, Frusca T, et al. Preterm delivery risk in migrants in Italy: an observational prospective study. J Travel Med. 2008;15:243–7.View ArticlePubMedGoogle Scholar
  32. Malin M, Gissler M. Maternal care and birth outcomes among ethnic minority women in Finland. BMC Public Health. 2009;9:84.View ArticlePubMedPubMed CentralGoogle Scholar
  33. Salvador S, Bertozzi S, Londeri AP, et al. Outcome of pregnancy for immigrant women: a retrospective study. Minerva Ginecol. 2010;62:277–85.PubMedGoogle Scholar
  34. Johnson EB, Reed SD, Hitti J, et al. Increased risk of adverse pregnancy outcome among Somali immigrants in Washington state. Am J Obstet Gynecol. 2005;193:475–82.View ArticlePubMedGoogle Scholar
  35. Essén B, Bödker B, Sjöberg NO, et al. Are some perinatal deaths in immigrant groups linked to suboptimal perinatal care services? BJOG. 2002;109:677–82.View ArticlePubMedGoogle Scholar
  36. Small R, Gagnon A, Gissler M, et al. Somali women and their pregnancy outcomes postmigration: data from six receiving countries. BJOG. 2008;115:1630–40.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Cervantes A, Keith L, Wyshak G. Adverse birth outcomes among native-born and immigrant women: replicating national evidence regarding Mexicans at the local level. Matern Child Health J. 1999;3:99–109.View ArticlePubMedGoogle Scholar
  38. Guendelman S, Buekens P, Blondel B, et al. Birth outcomes of immigrant women in the United States, France, and Belgium. Matern Child Health J. 1999;3:177–87.View ArticlePubMedGoogle Scholar
  39. Acevedo-Garcia D, Soobader MJ, Berkman LF. The differential effect of foreign-born status on low birth weight by race/ethnicity and education. Pediatrics. 2005;115:e20–30.View ArticlePubMedGoogle Scholar
  40. Agudelo-Suárez AA, Ronda-Pérez E, Gil-González D, et al. Relationship in Spain of the length of the gestation and the birth weight with mother’s nationality during the period 2001-2005. Rev Esp Salud Publica. 2009;83:331–7.View ArticlePubMedGoogle Scholar
  41. Gissler M, Alexander S, MacFarlane A, et al. Stillbirths and infant deaths among migrants in industrialized countries. Acta Obstet Gynecol Scand. 2009;88:134–48.View ArticlePubMedGoogle Scholar
  42. Cacciani L, Baglio G, Rossi L, et al. Hospitalisation among immigrants in Italy. Emerg Themes Epidemiol. 2006;3:4.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Newburn-Cook CV, Onyskiw JE. Is older maternal age a risk factor for preterm birth and fetal growth restriction? A systematic review. Health Care Women Int. 2005;26:852–75.View ArticlePubMedGoogle Scholar
  44. Bollini P, Pampallona S, Wanner P, et al. Pregnancy outcome of migrant women and integration policy: a systematic review of the international literature. Soc Sci Med. 2009;68:452–61.View ArticlePubMedGoogle Scholar
  45. Astolfi P, Zonta LA. Risks of preterm delivery and association with maternal age, birth order, and fetal gender. Hum Reprod. 1999;14:2891–4.View ArticlePubMedGoogle Scholar
  46. Hsieh TT, Liou JD, Hsu JJ, et al. Advanced maternal age and adverse perinatal outcomes in an Asian population. Eur J Obstet Gynecol Reprod Biol. 2010;148:21–6.View ArticlePubMedGoogle Scholar
  47. Shah PS. Knowledge synthesis group on determinants of LBW/PT births: parity and low birth weight and preterm birth: a systematic review and meta-analyses. Acta Obstet Gynecol Scand. 2010;89:862–75.View ArticlePubMedGoogle Scholar
  48. Shah PS, Zao J, Ali S. Maternal marital status and birth outcomes: a systematic review and meta-analyses. Matern Child Health J. 2011;15:1097–109.View ArticlePubMedGoogle Scholar
  49. Rasmussen F, Oldenburg CE, Ericson A. Preterm birth and low birthweight among children of Swedish and immigrant women between 1978 and 1990. Paediatr Perinat Epidemiol. 1995;9:e441–54.View ArticleGoogle Scholar
  50. Raatikainen K, Heiskanen N, Heinonen S. Does unemployment in family affect pregnancy outcome in conditions of high quality maternity care? BMC Public Health. 2006;6:46.View ArticlePubMedPubMed CentralGoogle Scholar
  51. Ouyang F, Zhang J, Betrán AP, Yang Z, Souza JP, Merialdi M. Recurrence of adverse perinatal outcomes in developing countries. Bull World Health Organ. 2013;91(5):357–67.View ArticlePubMedPubMed CentralGoogle Scholar
  52. Milne F, Redman C, Walker J, Baker P, Bradley J, Cooper C, et al. The pre-eclampsia community guideline (PRECOG): how to screen for and detect onset of preeclampsia in the community. BMJ. 2005;330:576–80.View ArticlePubMedPubMed CentralGoogle Scholar
  53. Bramham K, Parnell B, Nelson-Piercy C, Seed PT, Poston L, Chappell LC. Chronic hypertension and pregnancy outcomes: systematic review and meta-analysis. BMJ. 2014;348:g2301.View ArticlePubMedPubMed CentralGoogle Scholar
  54. Metzger BE, Lowe LP, Dyer AR, Trimble ER, Chaovarindr U, et al. Hyperglycemia and adverse pregnancy outcomes. N Engl J Med. 2008;358:1991–2002.View ArticlePubMedGoogle Scholar
  55. Hartling L, Dryden DM, Guthrie A, Muise M, Vandermeer B, et al. Benefits and harms of treating gestational diabetes mellitus: a systematic review and meta-analysis for the U.S. preventive services task force and the National Institutes of Health Office of Medical Applications of Research. Ann Intern Med. 2013;159:123–9.View ArticlePubMedGoogle Scholar
  56. Sato R, Ikuma M, Takagi K, Yamagishi Y, Asano J, Matsunaga Y, Watanabe H. Exposure of drugs for hypertension, diabetes, and autoimmune disease during pregnancy and perinatal outcomes: an investigation of the regulator in Japan. Medicine (Baltimore). 2015;94:e386.View ArticleGoogle Scholar
  57. Witcher PM, Chez BF, Baird SM. Multisystem effects of hypertensive disorders of pregnancy: a comprehensive review. J Perinat Neonatal Nurs. 2015;29:229–39.View ArticlePubMedGoogle Scholar

Copyright

© The Author(s). 2017

Advertisement

BMC Pregnancy and Childbirth

ISSN: 1471-2393

Contact us