Differences in perinatal morbidity and mortality on the neighbourhood level in Dutch municipalities: a population based cohort study
Vos et al. BMC Pregnancy and Childbirth
Differences in perinatal morbidity and mortality on the neighbourhood level in Dutch municipalities: a population based cohort study
Amber A. Vos 0
Semiha Denktaş 0 2
Gerard JJM Borsboom 1
Gouke J. Bonsel 0 1
Eric AP Steegers 0
0 Department of Obstetrics and Gynecology, Division of Obstetrics & Prenatal Medicine, Erasmus MC , PO Box 2040, 3000 CA Rotterdam , The Netherlands
1 Department of Public Health, Erasmus University Rotterdam , P.O. Box 2040, 3000 CA Rotterdam , The Netherlands
2 Department of Social Sciences, Erasmus University College, Erasmus University Rotterdam , PO Box 1738, 3000 DR Rotterdam , The Netherlands
Background: In a national perinatal health programme, we observed striking heterogeneity in the explanation of the most prominent risks across municipalities. Therefore we explored the separate contribution of several socio-demographic risks on perinatal health inequalities between municipalities and neighbourhoods. The study aims to identify perinatal health inequalities on the neighbourhood level across the selected municipalities, and to objectify the contribution of socio-demographic risk factors on pregnancy outcomes in each municipality by the application of the population attributable risk concept. Methods: Population based cohort study (2000-2008). Perinatal outcomes of 352,407 single pregnancies from 15 municipalities were analysed. Odds ratios and population attributable risks were calculated. Main outcomes were combined perinatal morbidity (small-for-gestational age, preterm birth, congenital anomalies, and low Apgar score), and perinatal mortality. Results: Perinatal health inequalities existed on both the municipal and the neighbourhood level. In municipalities, combined perinatal morbidity ranged from 17.3 to 23.6 %, and perinatal mortality ranges from 10.1 to 15.4 ‰. Considerable differences in low socio-economic status between municipalities were apparent, with prevalences ranging from 14.4 to 82.5 %. In seven municipalities, significant differences between neighbourhoods existed for perinatal morbidity (adjusted OR ranging from 1.33 to 2.38) and for perinatal mortality (adjusted OR ranging from 2.06 to 5.59). For some municipalities, socio-demographic risk factors were s a strong predictor for the observed inequalities, but in other municipalities these factors were very weak predictors. If all socio-demographic determinants were set to the most favourable value in a predictive model, combined perinatal morbidity would decrease with 15 to 39 % in these municipalities. Conclusions: Substantial differences in perinatal morbidity and mortality between municipalities and neighbourhoods exist. Different patterns of inequality suggest differences in etiology. Policy makers and healthcare professionals need to be informed about their local perinatal health profiles in order to introduce antenatal healthcare tailored to the individual and neighbourhood environment.
It is becoming increasingly clear that health inequalities in
western countries are also expressed in adverse perinatal
outcomes, such as preterm birth, growth restriction, and
perinatal mortality. These adverse perinatal outcomes are
especially observed in deprived districts and are often
associated with socio-economic and ethnicity related risk
factors such as low education, low-income and poor
integration into society. Socio-economic status and
neighbourhood deprivation are most consistently related
to these adverse outcomes [1–4]. Socio-economic status
can induce adverse perinatal outcome though multiple
pathways, but most importantly through low education
and low income levels . However, it is still unclear to
what extent the effect of neighbourhood deprivation goes
beyond the effect of poor level of socio-economic status at
the individual level .
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Two consecutive reports on perinatal health revealed a
relatively unfavourable position of the Netherlands
regarding perinatal mortality [7, 8]. Subsequent nationwide cohort
studies revealed an equally high impact on perinatal
outcomes of non-medical risk factors (e.g. social or lifestyle)
compared to medical and obstetrical risk factors [9–11]. In
order to gain more insight into these causes and their
impact, the concept of ‘Big 4 morbidities’ was introduced
. This study showed that four specific defined
conditions precede perinatal mortality in 85 % of all cases of
perinatal mortality, namely small for gestational age
(birth weight < 10th percentile for gestational age) ,
preterm birth (birth < 37 weeks of gestation), congenital
disorders, and/or low Apgar score (<7 after 5 min).
Taking this prior knowledge into account, the Healthy
Pregnancy 4 All (HP4All) study was initiated to improve
perinatal health and to generate effective strategies in
disadvantaged areas in the Netherlands . This national study
was supported by the Dutch Ministry of Health, Welfare
and Sport and combines epidemiologic and health services
research to evaluate the effectiveness of two obstetric
interventions in preconception care and antenatal healthcare.
Municipalities were selected to participate according to
socio-demographic data (high risk load) and perinatal
outcome data (high adverse outcome prevalence) .
Part of the initial fieldwork included consultation with
local stakeholders (e.g. caregivers, policy makers) to identify
reasons for deprivation. We observed a striking
heterogeneity in the explanation of the most prominent risks across
municipalities. Unlike the hypothesised homogeneity in
deprived areas as known from findings described above,
the differences in relative weight of socio-economic and
ethnicity related risk factors were much more divergent
according to these local stakeholders.
We therefore explored the separate contributions of
several socio-demographic risks in neighbourhood
perinatal health inequalities in more detail. This study aims
(1) to identify perinatal health inequalities on the
neighbourhood level across the selected municipalities, and
(2) to objectify the contribution of socio-demographic
risk factors on pregnancy outcomes in each municipality
by the application of the population attributable risk
The selection of the 15 municipalities took place within the
Healthy Pregnancy 4 All study. In a thorough preparatory
analysis, 50 geographical areas (municipalities) were
identified in which adverse perinatal outcomes were high. The
list was obtained by combining epidemiological evidence
on adverse outcomes from the national perinatal registries
of midwives, obstetricians and paediatricians [14, 15, 16].
The 15 municipalities showing the highest perinatal
morbidity and mortality rates were selected. All selected
municipalities have an above average perinatal mortality
rate and have numerous disadvantaged neighbourhoods.
Data of pregnant women from these 15 selected Dutch
municipalities were analysed in this study. The detailed
selection process of these municipalities was described
The study was conducted in the Dutch antenatal healthcare
system. The organisation of the Dutch perinatal care system
is unique as, in contrast to most other western countries,
midwifery and obstetric care is delivered by primary,
secondary and tertiary healthcare providers who function
autonomously. At the primary level of care, community
midwives provide care to pregnant women with an
assumed or estimated low risk for complications during
pregnancy and childbirth. Women allocated by the midwife
to this low-risk status can opt for a home birth or for an
out-patient hospital birth under supervision of their own
community midwife. Around 80 % of all pregnant women
start their antenatal health care in the primary level of care.
If complications threaten to occur during pregnancy or
during delivery, women are referred to an obstetrician in a
secondary or tertiary hospital .
Data from all singleton pregnancies in 15 selected
municipalities over the period 2000–2008 were obtained from
the Dutch Perinatal Registry (PRN). The PRN committee
gave ethical approval for this study (amendment on
application 11.36). As the database protects the anonymity of
the included pregnant women and data were analysed
anonymously, their written consent was not needed.
This registry contains detailed population-based
information on pregnancies, deliveries, and neonatal (re)admissions
until 28 days postpartum, recorded at the level of the child.
Source data were obtained by validated linkage of three
independent registries: the midwife registry (routinely
collected by 94 % of the midwives), obstetrics registry
(collected by 99 % of the obstetricians), and pediatric
registry (68 % of the paediatricians including 100 % of the
Neonatal Intensive Care Unit (NICU) paediatricians)
[15, 16]. Registration of midwifery and obstetric data starts
at the first antenatal visit, and complete perinatal data is
available from 20 weeks of gestation. The neonatal registry
only contains data on hospital admissions of neonates
following delivery. Overall, the PRN contains data of > 97 %
of all pregnancies in the Netherlands.
For determination of neighbourhood boundaries, we
used both 4-digit post codes and municipal
neighbourhood boundaries, as established by the national Central
Bureau for Statistics (CBS) in 2012 (open access). This
last institute is responsible by law for the subdivision of
all municipalities in the Netherlands into districts and
neighbourhoods amongst others for statistical purposes.
This subdivision is based on existing municipal
boundaries which occasionally do not coincide with four-digit
post code boundaries. As our primary data were post
code based, in those instances the post code was assigned
to the neighbourhood with the largest share in that
particular post code. An exception was made for ‘The
Hague’. Historically, this municipality has a different post
code classification system resulting in 44 neighbourhoods
with many overlapping post codes. We therefore
combined several adjacent neighbourhoods and reduced the
number to 24 neighbourhoods which allowed for adequate
projection of post codes to neighbourhoods.
Neighbourhoods containing industrial areas were generally excluded
because these areas are non-residential.
Primary outcomes and determinants
Primary outcomes were perinatal morbidity (Big four) and
perinatal mortality. Big four was defined as the presence
(single or combined) of small for gestational age (SGA)
(birth weight < 10th percentile for gestational age) ,
preterm birth (PTB) (birth < 37 weeks of gestation), congenital
disorders (list defined), and/or suboptimal start at birth
(Apgar score < 7 after 5 min). Perinatal mortality rate was
defined as death in the period from 22 weeks gestational
age until 7 days postpartum per 1000 births.
Socio-demographic risk factors included socio-economic
status (SES), ethnicity (western, non-western), parity
(nulliparous, multiparous) and maternal age. Data on
socioeconomic status was made available by The Netherlands
Institute for Social Research (open access) and provided as
status scores on post code level. The SES status scores were
composed of four indicators: the average household income
per particular post code, the proportion of residents with a
low family income, the proportion of poorly educated
residents and the proportion of unemployed residents in a
particular post code. We divided these status scores into
tertiles: below the 20th percentile, between the 20th and
80th percentile, or above the 80th percentile. The post code
comprises of socio-economically rather homogeneous small
areas with about 25–50 newborn per year. This data was
individually linked to the birth record database .
Ethnicity was assigned by the caregiver according to the
classification of the PRN. The PRN defines ‘ethnicity’ along
seven categories in line with the formal guidelines of the
CBS: Western Dutch, Western other (including women
from other European countries, Australia, and the United
States), and non-Western: Mediterranean, (East) Asian,
African, South Asian, or other non-Western. The
classification of ethnicity recorded in the PRN was made by the
health care professional and is typically based on a woman’s
appearance, name, and information provided in the context
of history taking (at least until January 2015). Note that
there is a distinction in the execution of classification
between the PRN database and the formal, governmental
CBS guidelines where classification was more nationality
based on the basis of the information provided by the
person (country of birth and parents’ country of birth).
The prevalence of perinatal morbidity and mortality was
analysed on the municipal level, and specifically within
the selected municipalities. We used data from the
years 2000–2008, with the total number of singleton
births as denominator. We restricted our data to all
singleton pregnancies in the 15 selected municipalities.
Logistic regression was used to study the relation
between perinatal morbidity and mortality, and the
neighbourhood of residence. The neighbourhood that had the
lowest prevalence of adverse perinatal morbidity was
chosen as the reference category. These analyses were
adjusted for individual factors such as SES, maternal age,
parity, ethnicity, and calendar year. In all analyses,
municipalities were analysed separately with a significance level
set at 0.05. All variables were tested for interaction, and
included when statistically significant. Above described
statistical analyses were performed using Statistical
Package of Social Sciences versions 20.0 for Windows
(SPSS Inc, Chicago, IL, USA).
Population attributable risks
In order to visualise the contribution of socio-demographic
risk factors on perinatal morbidity in each municipality, we
calculated the population-attributable risk (PAR)
percentages. The PAR of a risk factor is the proportion of disease
(i.e. pregnancy outcomes) that can be attributed to a
specific risk factor only among individuals with the risk
factor . In the standard formula, PAR estimations are
subject to limitations because the formula is not additive if
multiple risk factors interact . Therefore, we followed
the staged approach as described by Poeran and colleagues
to estimate perinatal morbidity in case selected risk factors
were hypothetically absent .
The aim of this analysis is to estimate the PAR of
sociodemographic risk factors. Therefore, we calculated the
PAR for two scenarios. In the first scenario, risk factors
were set to ‘the most favourable values’ in terms of
outcome whereby all women were ‘assigned’ to the highest
SES category (>p80), multiparous, western ethnicity, and
25–29 years old. In the second scenario, risk factors were
set to ‘more reasonable values’. Only the women in
extreme categories were reassigned: women in the low SES
category were assigned to middle SES category (20–p80)
and women aged < 18 years or > 35 years were assigned to
the reference category ‘25–29 years’. The values in the
original dataset remained unchanged.
To estimate perinatal morbidity (Big four), we created a
duplicate dataset in which the outcome variables were set
to ‘missing values’. We fitted a multivariate logistic
regression model on the original dataset to calculate predicted
values. The predicted values obtained from the fitted
model were used to predict the number of Big four cases
for both scenarios, the ‘most favourable values’ and ‘more
reasonable values’. For example, the expected number of
Big 4 cases in the ‘most favourable scenario’ was estimated
by applying the predicted values from the fitted
multivariate logistic regression model to the duplicate dataset in
which all women were hypothetically reassigned to above
listed scenario (e.g. highest SES category).
Finally, the observed Big four cases in the original dataset
were compared to the predicted cases of Big four for both
duplicate datasets. PARs were estimated as the proportional
change of the predicted and observed cases. For this
analysis, we used the GLIMMIX procedure in SAS version
9.2 to calculate the predicted values of perinatal morbidity
(SAS Institute Inc., Cary, NC).
A total of 352,407 singleton pregnancies were analysed.
The number of pregnancies per neighbourhood ranged
from 105 to 16,614 (mean of 2908 pregnancies per
As a total of 1,584,800 births occurred in the Netherlands
during 2000–2008, our study represents 22 % of all births.
Considerable differences in prevalences of low SES
(prevalences ranging from 14.4 to 82.5 %) and non-Western
ethnicity (prevalences ranging from 8.8 to 47.8 %) were
apparent across municipalities (Table 1).
SGA (ranging from 6.9 to 10.3 %) and PTB (ranging
from 5.6 to 7.8 %) determined the largest part in Big four
outcomes (17.3–23.6 %) (Table 2). In the years 2000–
2008, the average perinatal mortality rate in the
Netherlands was 9.5 ‰. In all municipalities, perinatal
mortality rates were higher than the national average
(10.1–15.4 ‰) (Table 2).
Almost all 15 municipalities showed significant
differences between neighbourhoods for both perinatal
morbidity and mortality rates. Differences were especially
large for perinatal mortality, in which the adjusted odds
ratios between the lowest and highest prevalence was 4
to 5 (Table 3). Analyses were adjusted for maternal age,
parity, ethnicity, SES, and calendar year effect. The
multivariate analysis for the municipality ‘Heerlen’ was
not applicable. In this particular municipality, there were
only low SES areas. Since neighbourhood and SES are
strongly correlated if not identical, it was not possible to
make a proper comparison between highest and lowest
categories. Interactions between all variables were found
to be non-significant. Overall, missing values were less
than 1 %. Missing values were set to the most favourable
values, e.g. all missing birth weights were set to ‘no SGA’
and included in the analysis (Table 3).
In Fig. 1 we displayed the observed Big four outcome of
pregnant women in each municipality (from the original
dataset), and the predicted Big four outcomes from the
duplicate dataset in case of ‘the most favourable’ and
Table 1 Characteristics of 15 studied Dutch municipalities in 2000–2008
Four villages in the
Four villages in the
Table 2 Perinatal morbidity and mortality rates of the 15 studied Dutch municipalities in 2000–2008
SGA (<p10), n (%)
Perinatal morbidity is defined as a combined measure of small for gestational age (SGA), preterm birth, congenital anomaly, and / or low Apgar score
‘most reasonable’ values with the corresponding PARs.
In both scenarios, the predicted Big four decreased in all
municipalities if socio-demographic risk factors were
With this figure we illustrated that the contribution of
socio-demographic risk factors to Big four outcomes
differed considerably among municipalities.
If-hypotheticallyall women would be multiparous, 25–29 years old, of
western ethnicity and correspond to the highest SES
category (above the 80th percentile), perinatal morbidity
would be reduced by 39 % in Rotterdam and 15 % in
Utrecht. In other words, the contribution of
socioeconomic risk factors was nearly one third for some
municipalities such as Enschede (33 %), Heerlen (39 %), and
Rotterdam (33 %), but appeared to be much lower for
others such as the municipalities Almere (16 %) and
Utrecht (15 %) (data not shown).
In this study we showed that patterns in perinatal health
inequalities differ on both the municipal and
neighbourhood level: some municipalities show overall high rates of
adverse perinatal outcomes, while others show large
differences between neighbourhoods. These neighbourhood
differences were particularly pronounced for perinatal
mortality. After adjustment for socio-demographic risk
factors, such as SES, maternal age, parity, and ethnicity,
these differences remained largely present.
These findings are in line with previous studies in which
area-level socio-economic variables, such as
neighbourhood income or poverty, remained significant after
adjustment of individual variables [10, 21, 22]. A previously
conducted study in the Netherlands also observed regional
differences within the Netherlands, but focused more on
care related factors such as travel time .
With the use of PARs, we tried to further explore
these differences across municipalities by calculating the
attribution of socio-demographic risk factors on adverse
outcomes. In some municipalities these risk factors are a
strong predictor for the observed inequalities by
explaining almost a third of the observed differences, while in
others their contribution seem less prominent. Behind
the general observation that perinatal morbidity and
mortality rates are high in these municipalities, different
mechanisms are apparently involved. This might be
attributed to other explanatory factors, not included in
this analysis, such as travel time to a hospital, place of
birth, child factors, organisational factors and/or
caregiver related factors which are all associated with adverse
pregnancy outcomes [11, 24–26]. Two other studies
conducted in the Netherlands also calculated PARs by
using the same data, but both focused on perinatal
mortality and were therefore not entirely comparable [11, 27].
Poeran and colleagues used the same PAR method as we
did in our analysis. They estimated the PAR for (the
combined effect of ) maternal, child and organisational factors.
They found a large reduction (over 94 %) in perinatal
mortality when all factors were set to the most favourable
value . However, this was a nationwide study without
focus on area-based differences.
Strengths and limitations
A major strength of this study was the usage of a
validated national perinatal dataset with an almost complete
Fig. 1 Observed and predicted perinatal morbidity in the 15 selected municipalities. Legend: The selected risk factors were set to ‘the more reasonable
values’ and to ‘most favourable values’. In the first scenario, only the women in extreme categories were reassigned: women in the low SES
category were assigned to middle SES category (20–p80) and women aged < 18 years or > 35 years were assigned to the reference category
‘25–29 years’. In the second scenario, all women were ‘assigned’ to the most favourable values: highest SES category (>p80), multiparous,
western ethnicity, and 25–29 years old. NA = not applicable (no cases in highest SES category), *four villages in the province Groningen
coverage of all pregnancies in the Netherlands over a
long period (2000–2008). This dataset includes many
variables on both risk and healthcare factors which
allowed for detailed analyses. Although the included
municipalities had higher rates of perinatal morbidity
and mortality than average, they represent one fifth of
all pregnancies in the Netherlands. Previous studies were
often municipality-based but not nationwide . By
including 15 different municipalities, we revealed major
differences between areas in a relatively small country
and high standards of health care.
This study also has some limitations. Area based
measures such as socio-economic status may not correspond
to the individual pregnant women, and do not reflect
heterogeneity among individuals, healthcare professionals
or other characteristic factors within a particular
neighbourhood. We dealt with three levels of data (individuals
that were clustered in a neighbourhood setting which
were in turn nested in cities), so one can consider the use
of multilevel type of analysis. Multilevel models address
the hierarchical nature of data. Clustering primarily
violates the independence of error assumption of most
other regression designs . The net effect of multilevel
models is a widening of confidence intervals of individual
effects, while careful comparison of differences between
random slope and random intercept models give an
impression on the degree to what level effects actually are
present. In the case of the selected 15 countries, we judged
that the use of multilevel-modelling was not beneficial.
Foremost, this is a comparison of selected cities and
neighbourhoods. This aimed a straightforward contrast,
rather than a complete neighbourhood study where we
felt that multi-level modelling would be more appropriate.
We phrased the findings in a non-exaggerating way, which
circumvents over optimism with the estimated (individual)
A limitation was the use of neighbourhood level SES
instead of a single individual variable which was
unavailable. It has been shown that without adequate control of
individual socio-economic factors, neighbourhood effects
might act as proxy for unmeasured aspects of unmeasured
individual factors . The post code size was small (on
average 50 deliveries per year) and post codes were
therefore also used as pseudo-individual SES indicator in for
example compensation payments to caregivers for the
assumed added services in deprivation areas. It has been
suggested that the choice of using neighbourhood level
variables may be less critical since this captures the
unmeasured individual level variation in outcomes and that
misspecification of the neighbourhood effect is less likely to
occur . However, the invincible use of post code
boundaries in our study did not always reflect the actual
neighbourhood boundary, which may be a limitation of this
study if adjacent neighbourhoods were contrasting [30, 31].
The two greatest advantages of using the PRN database
were the large amount of data from pregnancies in the
Netherlands that became available over time (over 1
million records) and the high rate of complete cases (more
than 97 %). However, by using the PRN database we also
faced some limitations. Firstly, the medical registry mainly
captures data on specific processes in the healthcare
process such as admissions or pregnancy complications.
Data on medical, social and pregnancy related risk factors
as well as the performance and outcome on prenatal
screening are lacking. Using this data for research
purposes, other requirements such as the amount and
quality of information also become important. One of the
disadvantages we faced in our study was the participation
of only 70 % of all pediatric wards in the Netherlands (and
100 % of the NICU facilities). This means that partial and
selective participation challenges the completeness of
short term neonatal outcome. The outcomes reported
here, however, are complete as these primarily are
recorded by midwife and obstetrician. Secondly, the lack
of data of some important maternal risk factors for
perinatal morbidity and mortality, such as level of education,
smoking during pregnancy, maternal body mass index and
folic acid intake was another important limitation of using
this database. Smoking is registered in the Perinatal
Registry, but this information was not used because of
underreporting (prevalence 0.5 %). Thirdly, we faced some
limitations in the approach of dichotomous grouping of
Western and non-Western women. By dichotomising
diverse ethnic groups, socio-demographic characteristics
may resemble but groups may differ with respect to
patterns of social status, health behavior, biological set up,
and consequently birth outcomes. As mentioned in our
second limitation, we were unable to study specific risk
factor patterns among various ethnic groups, if they
should exist. In addition, this approach might result in an
oversimplification as this dichotomy might lead to the
perception that all non-Western ethnic groups are ‘the
same’, reflecting a uniform problem . As we were
primarily interested in examining perinatal health
inequalities on the neighbourhood level across the selected
municipalities, and to objectify the contribution of
sociodemographic risk factors on adverse pregnancy outcomes
in each municipality by the application of the population
attributable risk concept, we opted for this dichotomous
classification of Western versus non-Western women. By
using two simplified two categories, we tried to evade a
potential misclassification due to the allocation of
ethnicity on basis of a woman’s appearance in the PRN
database. This dichotomous approach was also used in
previous studies [32, 33].
Practical implications and future research
In this study, we observed marked differences in perinatal
outcomes across municipalities. We observed different
patterns in these disparities: some showed high rates of
perinatal morbidity and/or mortality, while others showed
large differences between neighbourhoods, or both.
Remarkably, socio-demographic risk factors were not always
associated with the observed inequalities.
With this study we also emphasise the importance of
tailor-made antenatal healthcare, which seems necessary
to encounter potential high risk pregnancies. We advise
policy makers and health care professionals to develop
additional local policy to define their high risk population,
e.g. by means of customised preconception care and
systemic risk assessment tailored to the individual and
social environment of both the woman and the working
area of a caregiver. This implicates that more research is
necessary to explore etiologic factors associated with
perinatal morbidity and mortality on regional level. In
2012 in the Netherlands regional so-called research
consortia were constituted to enhance local collaboration
which could anticipate to our findings. In addition, more
research is necessary to develop specific recruitment
strategies to timely reach high risk populations.
In conclusion, substantial differences in perinatal morbidity
and mortality between municipalities and neighbourhoods
exist. Socio-demographic risk factors in municipalities are
not always a strong predictor for the observed inequalities,
implicating that different mechanisms are involved. Our
findings suggest that the identification of perinatal
morbidity and mortality rates, organisational features of care and
etiologic factors on regional level are a valuable first step to
customise antenatal healthcare.
Availability of supporting data
The data set supporting the results of this article is available
via Central Bureau of Statistics (http://www.cbs.nl/nl-NL/
default.htm.) and via The Netherlands Institute for Social
HP4All: Healthy pregnancy 4 All; NICU: Neonatal intensive care unit; OR: Odds
ratio; PAR: Population attributable risk; PRN: Perinatal registry Netherlands;
R4U: Rotterdam reproductive risk reduction; PTB: Preterm birth; SES:
Socio-economic status; SGA: Small for gestational age.
AV developed the concept for the study, analysed the data and wrote the
first draft of the manuscript.
SD developed the concept for the study, helped to analyse the data and
wrote the first draft of the manuscript. ES developed the concept for the
study, participated in the study design and revised the draft manuscript for
intellectual content. GJJMB helped to analyse the data. GJB participated in
the study design and helped to analyse and to interpret the data. All authors
contributed to the interpretation of the results and critical revision of the
manuscript for important intellectual content and read and approved the
final version of the manuscript.
The Healthy Pregnancy 4 All study is made possible by financial support
from the Dutch Ministry of Health, Welfare and Sport, The Hague, grant
318,804. Special thanks goes to The Netherlands Perinatal Registry that kindly
provided permission for data analysis (amendment on number 11.36).
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