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Conteúdo (article):

Introduction
Hypertensive syndromes during pregnancy (HSP) are among the main causes of
maternal and perinatal morbidity and mortality 1,2 . Worldwide, HSPs occur in approximately
3% to 14% of all pregnancies 3 . In Brazil, a cross-sectional study conducted by Oliveira et al. 4
found that of the 12,272 pregnant women studied, 10.2% had HSP. Leal et al. 5 , based on data
from the Pelotas birth cohort in 2015, found that 31.3% of the women had HSP. In the
epidemiological context, the frequency of HSP varies greatly due to the different
characteristics of the population, definitions, and diagnostic criteria used 6 .
The diagnostic criterion for HSP is the presence of hypertension greater than or equal
to 140/90 mmHg on two occasions with a minimum interval of four hours 7 . The most
commonly accepted recommendations come from the American College of Obstetricians and
Gynecologists 1 , which classifies HSP into four categories: chronic arterial hypertension,
gestational hypertension, preeclampsia (PE)/eclampsia, and PE superimposed on chronic
hypertension. More recently, the Guidelines of the Brazilian Network of Studies on
Hypertension in Pregnancy 8 classify HSP into five categories: chronic arterial hypertension,
white coat syndrome, gestational hypertension, preeclampsia (PE)/eclampsia, and PE
superimposed on chronic hypertension.
Prematurity is one of the neonatal outcomes most frequently associated with HSP and
can occur because of the interruption of pregnancy due to maternal and/or fetal compromise
or spontaneous labor due to increased uterine contractility 9 . Premature birth represents a major
challenge for public health services worldwide 10 , and is considered to be the main risk factor
for infant morbidity and mortality 11 .
In the systematic review conducted by Chawanpaiboon et al. 11 , data from 139 million
live births in 2014 were evaluated. The authors identified that the frequency of premature
births ranged from 8.7% in Europe and 13.4% in North Africa, while in Brazil this finding

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was 11.2%, which ranked Brazil 9th among 10 countries with the highest frequencies of
premature births. Although previous studies have already evaluated the association between
HSP and prematurity 12,13 , Franco et al. 6 in a recent integrative literature review highlighted the
presence of methodological differences between these studies, especially with regard to the
classification of HSP and prematurity, and the control of confounding factors.
The present study therefore aimed to evaluate the causal effect of HSP on early and
late preterm birth based on national data. In addition, the present study is justified by
evaluating the causal effect of HSP on early and late preterm birth using the directed acyclic
graph (DAG), a graph tool used to identify the covariates that may or may not confound this
causal relationship 14 , and the propensity score, a statistical technique to evaluate the effects of
treatment (exposure) on the outcome when quasi-experimental or observational data are
used 15 .

Methods
Study design and population
This article is part of a nationwide, hospital-based cross-sectional study entitled
“Nascer no Brasil: Inquérito Nacional sobre Parto e Nascimento” (“Born in Brazil: National
Survey on Childbirth and Birth”), conducted between 2011 and 2012. The STROBE 16
guideline was used to guide the writing of all sections of this study. The sample for the larger
study was selected in three stages. In the first stage, hospitals with more than 500 births per
year were stratified according to the five macro-regions of the country, location (capital or
interior) and type of service (public, mixed, or private), while 266 hospitals were selected
with a probability of selection proportional to the number of births in each of the strata in
2007. In the second stage, the number of days needed to interview 90 postpartum women in
each hospital (minimum of 7 days) was defined using an inverse sampling method. In the

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third stage, eligible women were selected. Ninety interviews were planned per hospital, and
23,894 women were interviewed. Details of the sampling design and selection of postpartum
women are available in Vasconcellos et al. 17 , and data collection can be found in Leal et al. 18 .
For the larger study, women who had a live birth, regardless of weight or gestational
age, or a stillbirth weighing ≥500 g and/or gestational age ≥22 weeks of gestation were
included, and those with severe mental disorders, who were deaf, or who did not understand
Portuguese were excluded 18 .

Exclusion criteria
The sample of the present study consisted of 20,494 women (18,125 without HSP and
2,369 with HSP). Of the number of newborns in the larger study (n = 24,200), 3,686 were
excluded for the following reasons: 489 twins, 61 without information on the presence of
HSP, 933 without information on the adequacy of prenatal care, two due to the absence of
data on maternal age, one due to the absence of data on parity, and 2,200 newborns with
gestational age ≥ 41 weeks. Of this number (20,514 newborns), 8,336 early term newborns
(37 and 38 weeks) were excluded, totaling 12,178 preterm and full-term births. Of this total,
534 were classified as early preterm birth; 1,605 were late preterm births, and 10,039 were
full-term newborns (between 39 and 40 weeks) (Figure 1).

Study variables
The exposure variable was the presence of HSP, which consisted of the summary of
positive responses to any question contained in the questionnaires completed with data from
hospital records and prenatal cards: “hypertension with continued treatment”, “hypertensive
syndromes during the current pregnancy (chronic hypertension, preeclampsia, or HELLP

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syndrome (H: hemolysis; EL: elevated liver enzymes; LP: low platelet count)”,
“eclampsia/seizure in the current pregnancy”, “diagnosis of eclampsia and seizure upon
admission”, “diagnosis of hypertension during pregnancy upon admission (any type)”, and
“high blood pressure outside of pregnancy”.
The outcome studied was early preterm birth (gestational age <34 weeks) and late
preterm birth (gestational age between 34 and 36 weeks). The gestational age considered to be
full-term (between 39 and 40 weeks and 6 days of gestation) was used as the reference
category, regardless of the onset of labor. Gestational age was estimated using an algorithm
based mainly on estimates from early ultrasound performed between 7 and 20 weeks of
gestation. In the absence of this examination, gestational age was based on information
reported by the postpartum women in the interview and, finally, on the date of the last
menstrual period 19 .
Newborns with a gestational age between 37 and 38 weeks (early term) and newborns
with a gestational age ≥ 41 weeks were excluded to ensure that the comparison group,
newborns with a gestational age between 39 and 40 weeks of gestation, had a lower
prevalence of factors related to early and late gestational age 20 .

Data analysis
Data analysis was performed in five stages. Initially, a DAG was developed based on a
broad bibliographic survey. This graph tool aimed to establish the relationship among
exposure (HSP), covariates, and outcomes (early and late preterm birth).
The DAG of the present study was developed in the DAGitty program (public domain,
available at http://www.dagitty.net/) developed to create, edit, and analyze causal models 21 .
This program follows strict DAG rules to identify the minimum set of variables to be adjusted

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for confounding factors in order to identify the causal effect 21 . This DAG is presented in
Figure 2.
To assess the pre-gestational nutritional status of women, pre-gestational BMI was
used based on the cutoff points recommended by the Institute of Medicine 24 . Total gestational
weight gain was calculated by subtracting the weight of the last prenatal visit from the pre-
gestational weight, both collected from the prenatal card or self-reported by the postpartum
woman. The adequacy of weight gain was corrected for gestational age at birth. For each
week less than 40 weeks (full-term gestation), the average weekly weight gain was discounted
from the minimum and maximum values ​​for each pre-gestational BMI range in the second
and third gestational trimesters.
To classify the adequacy of prenatal care, the gestational trimester at the time of the
start of prenatal care, the number of consultations attended corrected for gestational age at the
time of delivery, routine exams performed, and the indication of the reference maternity
hospital for childbirth care were considered, which was considered based on the mother's
report on the medical advice received. Prenatal care was considered adequate when prenatal
care began up to 12 weeks of gestation and 100% of the minimum consultations scheduled for
the gestational age at the time of delivery had been completed, according to the
recommendation of the Rede Cegonha 25 in effect during the broader study 26 .
After the DAG had been performed, the second stage consisted of a descriptive
analysis of maternal, prenatal and postpartum characteristics according to the presence or
absence of HSP. and the analyzed outcomes (early and late preterm birth). In this stage, the
Chi-square test with Rao-Scott adjustment 27 was used to compare the proportions between the
exposed and unexposed groups. The significance level adopted was 5%. In the third stage,
bivariate logistic regression was performed between the adjustment covariates contained in
the minimum set of the DAG (maternal age, maternal education, marital status, pre-

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gestational BMI, gestational weight gain, adequacy of prenatal care, parity, anemia, pre-
gestational diabetes mellitus, gestational diabetes mellitus, autoimmune disease (systemic
lupus erythematosus), chronic kidney disease, urinary tract infection), and the outcomes (early
and late preterm birth), using full-term newborns as the reference. The results were expressed
as odds ratios (OR) with their respective 95% confidence intervals (95% CI).
The propensity score weighting method was then applied, which consists of assuming
interchangeability, i.e., treated/exposed individuals are similar to untreated/control individuals
in such a way that they could be in either group if the exposure was not different between
them 28 . Once the selection probabilities for each woman had been estimated, the next step was
to weigh the estimate by the inverse of the selection probability 29 . With this method, the aim is
to compensate for differences by assigning greater weights to less common observations and
lower weights to more frequent ones in an attempt to achieve balance in the study 29 .
After estimation and weighting with the propensity score, the fourth stage verified the
balance of the groups in relation to the adjustment covariates that could interfere in the
relationship between the exposure and the outcome using the absolute standardized difference
of the means. The balance was verified before and after the implementation of the propensity
score and was considered adequate when less than 0.1021.
In the fifth stage, the crude and adjusted odds ratios between HSP and early and late
preterm birth were calculated with the respective 95% CI. The analyses were performed using
the R software, version 3.4.2 (The R Foundation for Statistical Computing), using the twang
library to estimate the propensity score.
In the statistical analysis, the complex sampling design was considered with the use of
weighting and calibration of the data and the incorporation of the design effect of 1.3 in order
to ensure that the distribution of the postpartum women in the sample was similar to that
observed in the population for the year 2011.

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Ethical aspects
The larger study was approved by the Research Ethics Committee (Comitê de Ética
em Pesquisa – CEP) of the Sérgio Arouca National School of Public Health, Oswaldo Cruz
Foundation (ENSP/Fiocruz), logged under opinion no. 92/10. For the purpose of the present
study, analysis and approval by the CEP of the Fernandes Figueira National Institute of
Women, Children, and Adolescents, Oswaldo Cruz Foundation (IFF/Fiocruz) was waived.

Results
In this study, data from 20,494 postpartum women were analyzed (18,125 without
HSP and 2,369 with HSP). Of this total, 10.4% had premature births, 2.6% of which were
early preterm births and 7.8% were late preterm births. Among women with HSP, the
frequency of premature births was 19.3%, 5.8% of which were early preterm births and 13.5%
were late preterm births.
When comparing the sociodemographic and clinical characteristics between women
with HSP and the others, it was observed that the frequency of HSP was higher among
women aged 12 to 19 years (20.2%), with incomplete elementary education (27.6%), without
a partner (18.3%), and without gestational diabetes mellitus (93.1%) (Table 1).
Early preterm births were more frequent among women aged 12 to 19 years (27.3%),
with incomplete primary education (38.6%), with low pre-gestational weight (9.9%), with
inadequate prenatal care (31.1%), and who had 3 or more births (16.9%), as compared to
women who had births between 39 and 40 weeks of gestation (Table 2).

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When analyzing women who had late premature births in relation to those who had
births between 39 and 40 weeks of gestation, a higher frequency of this outcome was
observed among women aged 12 to 19 years (24%), with incomplete elementary education
(30.8%), without a partner (20.7%), with low pre-gestational (10.2%), with insufficient
gestational weight gain (28.5%), with inadequate prenatal care (30.9%), and with 3 or more
births (14.5%) (Table 2). In the logistic regression analysis, it was observed that the variables
that increased the chance of early preterm birth were ages between 12 and 19 years (OR: 1.90;
95% CI: 1.55-2.32), incomplete elementary education (OR: 3.02; 95% CI: 2.18-4.18), low
pre-gestational weight (OR: 1.35; 95% CI: 1.10-1.82), inadequate prenatal care (OR: 2.19;
95% CI: 1.75-2.74), parity with 3 or more births (OR: 2.24; 95% CI: 1.73-2.89), and urinary
tract infection (OR: 1.41; 95% CI: 1.14-1.75). The protective variables for early preterm birth
were age over 35 years (OR: 0.58; 95% CI: 0.41-0.83), pre-gestational obesity (OR: 0.68;
95% CI: 0.48-0.95), more than adequate prenatal care (OR: 0.49; 95% CI: 0.37-0.65), and
gestational diabetes mellitus (OR: 0.55; 95% CI: 0.38-0.80) (Table 3). It was observed that
the variables that increased the chance of late preterm birth were ages between 12 and 19
years (OR: 1.62; 95% CI: 1.42-1.84), incomplete elementary education (OR: 3.35; 95% CI:
2.67-4.19) and complete high school (OR: 1.72; 95% CI: 1.38-2.15), having a partner (OR:
1.36; 95% CI: 1.91-1.55), low pre-gestational weight (OR: 1.38; 95% CI: 1.15-1.65),
inadequate prenatal care (OR: 2.36; 95% CI: 2.05-2.72), and parity with 3 or more births (OR:
1.88; 95% CI: 1.59-2.22). The protective variables for this outcome were age over 35 years
(OR: 0.35; 95% CI: 0.55-0.81), pre-gestational obesity (OR: 0.61; 95% CI: 0.49-0.75), more
than adequate prenatal care (OR: 0.56; 95% CI: 0.47-0.66), gestational diabetes mellitus (OR:
0.52; 95%CI: 0.41-0.65), and urinary tract infection (OR: 0.54; 95% CI: 0.45-0.64) (Table 3).
It was found that after balancing the information, all mean differences for the
covariates used to weight women were below 0.10, indicating that balancing after adjustment
by the propensity score was adequate (data presented in supplementary material).

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Women with HSP had a 3.34-fold greater chance of early preterm birth (OR: 3.34;
95% CI: 2.72-4.10) and a 2.41-fold greater chance of late preterm birth (OR: 2.41; 95% CI:
2.09-2.77). After analysis by propensity score, it was found that these women had a 2.74-fold
greater chance of early preterm birth (ORadj: 2.74; 95% CI: 2.12-3.54) and a 2.40-fold greater
chance of late preterm birth (ORadj: 2.40; 95% CI: 1.86-3.08) (Table 4).

Discussion
This study evaluated the causal effect of HSP on early and late preterm birth. The
prevalence of HSP was 11.5%, similar to that found in the cross-sectional study conducted by
Bacelar et al. 30 , conducted with 2,960 women (13.4%). Regarding preterm birth, it was
observed that the frequency of this outcome (19.3%) was almost twice as high among women
with HSP when compared to the others, with a predominance of late preterm births (13.5%)
as compared to early preterm births (5.8%).
Using the propensity score weighting method, the causal effect of HG on early
(ORadj: 2.74; 95% CI: 2.12-3.54) and late (ORadj: 2.40; 95% CI: 1.86-3.08) preterm birth
was observed. It is important to mention that although some researchers have analyzed the
association between HSP and prematurity 12,13 , there are still few studies that have focused on
the use of the methods employed to analyze the data in this study.
In Brazil, the cross-sectional study by Rezende et al. 31 , conducted with 4,464 pregnant
women, found an association between PE and early prematurity (PR: 11.01; 95% CI: 7.21-
14.80). In a cohort of 28,967 British pregnant women, Love et al. 32 analyzed the association
between gestational hypertension, PE, and early (<32 weeks of gestation) and late (33 to 36
weeks of gestation) prematurity, and found that women with gestational hypertension had a
higher chance of early prematurity (OR: 1.55; 95% CI: 1.29-1.80), while those with PE had a
higher chance of both early (OR: 4.27; 95% CI: 3.46-5.38) and late (OR: 1.55; 95% CI: 1.29-

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1.87) prematurity. When adjustment for maternal socioeconomic conditions was
implemented, the association remained between PE and early and late prematurity.
Similarly, Johnson et al. 33 , based on a cohort study of 14,170 women, found that after
adjusting for maternal socioeconomic factors, pregnant women with HSP had a 40% risk of
prematurity (RRadj: 1.4; 95% CI: 1.3-1.6). Furthermore, when individually evaluating the
components of HSP, the authors observed that the risk for this outcome was higher among
pregnant women with PE (RR: 2.5; 95% CI: 2.2-2.8), followed by women with chronic
hypertension (RR: 2.3; 95% CI: 2.1-2.6) and with gestational hypertension (RR: 1.2; 95% CI:
1.1-1.3). Regarding the methods used to assess the causal effect of HSP on early and late
preterm birth, it is important to briefly discuss the DAG and the propensity score weighting
method. It is important to highlight that the assessment of causality is an extremely important
issue for epidemiology and has been the subject of intense study for at least three centuries 34 .
It is understood that the study of the causal relationship between HSP and prematurity
is quite complex and involves a network of socioeconomic 35,36 , clinical 37,38 , genetic 39,40 ,
healthcare 41,42 , and nutritional 43,44 factors. Thus, to deal with these multiple risk factors, the use
of graph models would be the appropriate strategy, given that they are performed based on a
flexible structure in order to explore the multidimensional determinants and complex causal
mechanisms 14 . In this case, the use of DAG proved to be an adequate tool for dealing with
consistent assumptions and multiple risk factors, in addition to allowing researchers to use
relatively simple and systematic graph criteria to identify a set of confounding variables that
need to be controlled in the analysis 21 .
Several points deserve to be highlighted in this study, including the novelty of
assessing the causal effect of HSP on prematurity in Brazil based on a nationwide sample of
public and private hospitals. The use of DAG to identify the minimum set of confounding
variables also stood out, and the analysis method applied was weighting by the propensity

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score. In this type of study, the use of the propensity score aims to reduce bias in the estimates
of the effect of exposure 15 , by recovering the interchangeability between the treated (exposed)
and untreated (unexposed) groups 15,45,46 . With this method, it is possible to find identical or
very similar individuals among the comparison groups, especially when all possible
confounding covariates can be observed in relation to the outcome of interest 47 . It is important
to note that prematurity in the present study was not assessed in a conventional manner,
considering a gestational age of less than 37 weeks 48,49 . Given that gestational age at birth is a
factor that impacts physiological maturity, the present study categorized prematurity into
early and late. The gestational period between the 34th and 36th weeks is essential for the
immunological, cerebral, and pulmonary development of the newborn 50 . Regarding
complications for premature infants, signs of severity are greater for early premature infants,
as they are more likely to receive active resuscitation at birth, respiratory support, and
parental nutrition during the hospitalization period, which requires a longer hospital stay 51 .
Moreover, late premature infants are still physiologically and metabolically immature,
presenting an increased risk of neonatal complications (respiratory distress, hypothermia,
hypoglycemia, hyperbilirubinemia, feeding difficulties, and infections) 51,52,53 .
This study does have its limitations. Although the minimum set of variables also
includes the variable interpregnancy interval, this was not included in the regression analysis,
as it was not collected in the larger study. Although an extensive literature review was carried
out to construct the DAG for this study, it is important to note that every graph may contain
errors, since the true causal structure is often unknown, due to the considerable limitation of
scientific knowledge 21 .
However, it is important to highlight that not using this approach due to uncertainty
about the veracity of the DAG simply demonstrates that chance, rather than rational
deliberation, is allowed when choosing between the different causal diagrams. Therefore,

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causal inference in observational studies will nearly always be a challenging exercise and will
depend on the validity of the chosen model 21 . Therefore, even in view of this issue, it is worth
noting that the diagram was constructed by experienced researchers in the maternal-child area
and that it can be used by other observational studies that have objectives similar to those of
the present study.
Considering that HSP have a causal effect on early and late prematurity, early
diagnosis and treatment of these syndromes should be analyzed during prenatal monitoring,
aiming at their reduction and possible short- and long-term consequences for the newborn.
Thus, it is assumed that the findings of the present study have important clinical implications,
in addition to being an aid in the planning and creation of public health policies aimed at
preventing these conditions, as well as in further studies in search of more effective
preventive and interventional strategies for this population during prenatal care.
Supplementary material has been deposited in the Scielo Data repository:
https://doi.org/10.48331/scielodata.JVLT2K

REFERENCES
1. ACOG. The American College of Obstetricians and Gynecologists. Practice Bulletin No.
202: Gestational Hypertension and Preeclampsia. Obstetrics & Gynecology
2019;133(1):e1–e25.
2. Wang W,  Xie X,  Yuan T, Wang Y, Zhao F, Zhouet Z, et al. Epidemiological trends of
maternal hypertensive disorders of pregnancy at the global, regional, and national levels: a
population‐based study. BMC Pregnancy and Childbirth. 2021;21(1):364.
3. Say L, Chou, D Gemmill A, Tunçalp, Ö, Moller, AB Daniels. Global causes of maternal
death: a WHO systematic analysis. Lancet Glob Health. 2014;2(6):e323-33.
4. Oliveira CA, Lins CP, Sá RAM, Netto HC, Bornia RG, Silva NR, et al. J. Síndromes
hipertensivas da gestação e repercussões perinatais. Revista Brasileira de Saúde Materno
Infantil. 2006; 6(1), 93–8.

16
5. Leal LF, Grandi SM, Miranda VIA, Dal Pizzol TDS, Platt RW, Silveira MF et al.
Hypertensive Disorders of Pregnancy and Medication Use in the 2015 Pelotas (Brazil) Birth
Cohort Study. International Journal of Environmental Research and Public Health.
2020;17(22).
6. Franco EP, Rocha DM, Gama SGN, Moreira MEL, Saunders C. Divergências
metodológicas entre os estudos que avaliaram a associação entre as síndromes hipertensivas
da gestação e a prematuridade: uma revisão integrativa da literatura. Research, Society and
Development. 2021;10(12): e591101220591.

7. NHBPEP. Report of the National High Blood Pressure Education Program Working Group
on High Blood Pressure in Pregnancy. American Journal of Obstetrics and Gynecology.
2000;183(1): s1–s22.
8. Peraçoli JC, Costa ML, Cavalli RC, de Oliveira LG, Korkes HA, Ramos JGL, et al. Pré-
eclâmpsia – Protocolo 2023. Rede Brasileira de Estudos sobre Hipertensão na Gravidez.
2023.
9. Brandi LDA, Rocha LR, Silva LS, Bretas LG, Rodrigues MA, Araújo STH. Fatores de
risco materno-fetais para o nascimento pré-termo em hospital de referência de Minas Gerais.
Revista Médica de Minas Gerais. 2020; 30; 41–7.
10. Berger AZ, Zorzim VI, Pôrto EF, Alfieri FM et al. Parto prematuro: características das
gestantes de uma população da zona sul de São Paulo. Revista Brasileira de Saúde Materno
Infantil. 2016;16:427-435.
11. Chawanpaiboon S, Vogel JP, Moller AB, Pisake L, Max P, Daniel H et al. Global,
regional, and national estimates of levels of preterm birth in 2014: a systematic review and
modelling analysis. The Lancet Global Health. 2019;7(1):e37–e46.
12. Antunes MB, Demitt MO, Gravena AAF, Padovani C, Pelloso SM. Síndrome hipertensiva
e resultados perinatais em gestação de alto risco. Revista Mineira de Enfermagem. 2017; 21,
1–6.
13. Lu CQ, Lin J, Yuan L, Zhou JG, Liang K, Zhong QH et al. Pregnancy induced
hypertension and outcomes in early and moderate preterm infants. Pregnancy hypertension.
2018;14, 68-71.

17
14. Hernán MA, Herández-Diaz S, Werler MM, Mitchell AA. Causal Knowledge as a
prerequisite for confounding evaluation: an application to birth defects epidemiology.
American Journal of Epidemiology. 2002; S155(2):176-184.
15. Patino, CM, Ferreira, JC. Escores de propensão: uma ferramenta para ajudar a quantificar
os efeitos de tratamento em estudos observacionais. Jornal Brasileiro de Pneumologia. 2017;
43:86-86.
16. Von EE, Altman DG, Egger M, Pocok SJ ,  Gøzsche , PC,  Vandenbroucke , JP. The
Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement:
guidelines for reporting observational studies. Lancet. 2007; 370:1453-79.
17. Vasconcellos MTL, Silva PLN, Pereira APE, Schilithz AC, Souza Junior, PR BD et al.
Desenho da amostra Nascer no Brasil: Pesquisa Nacional sobre Parto e Nascimento. Cad
Saúde Pública. 2014;30:S49–58.
18. Leal MC, Silva AAM, Dias MAB, Gama, SGN, Rattner D, Moreira ME et al. Birth in
Brazil: national survey into labour and birth. Reprod Health. 2012;9-15.
19. Pereira APE, Leal MC, Gama SGN, Domingues RMSM, Schilithz AOC, Bastos, MH et
al. Determining gestational age based on information from the Birth in Brazil study. Cad
Saúde Pública. 2014;30:S59–70.
20. Leal MC, Esteves-Pereira AP, Nakamura-Pereira M, Domingues RMSM, Dias MAB,
Moreira ME et al. Burden of early-term birth on adverse infant outcomes: a population-based
cohort study in Brazil. BMJ Open. 2017;7(12): e017789.
21. Silva AAM. Introdução à Inferência Causal em Epidemiologia: uma abordagem gráfica e
contrafactual. 1st ed. Editora Fiocruz: Brasil. 2021.
22. Pearl J. The art and science of cause and effect. In Causality: models, reasoning and
inference. Cambridge University of Cambridge. 2000;331-358.
23. Shrier I, Platt RW. Reducing bias through directed acyclic graphs. BMC Med Res
Methodol. 2008; 8:70.
24. Institute of Medicine. Institute of Medicine, National Research Council (US) Committee
to Reexamine IOM Pregnancy Weight Guidelines; Rasmussen KM, Yaktine AL, Editors.
Weight Gain during Pregnancy: Reexamining the Guidelines. Washington (DC): The
National Academis Press. 2009.

18

25. Brasil M da S. PORTARIA N o 1.459, DE 24 DE JUNHO DE 2011. Institui, No Âmbito
Do Sistema Único de Saúde - SUS - a Rede Cegonha. 2011.
http://bvsms.saude.gov.br/bvs/saudelegis/gm/2011/prt1459_24_06_2011.html
26. Domingues RMSM, Viellas EF, Dias MAB, Torres JA, Theme-Filha MM, Gama SGND
et al. Adequação da assistência pré-natal segundo as características maternas no Brasil. Rev
Panam Salud Publica. 2015;37:140–147.
27. Rao JNK; Scott AIJ. The analysis of categorical data from complex sample surveys: chi-
squared tests for goodness of fit and independence in two-way tables. Journal of the American
statistical association. 1981; 6, 374(6);221-230.
28. D’Agostino Jr RB. Propensity score methods for bias reduction in the comparison of a
treatment to a non-randomized control group. Statistics in Medicine.1998;17(19):2265–81.
29. Paes A. Uso de Escores de Propensão Para Corrigir Diferenças Entre Grupos.
2012;10(3):103–104.
30. Bacelar EB, Costa MCO, Gama SGN, Amaral MTR, Almeida AHV et al. Fatores
associados à Síndrome Hipertensiva Específica da Gestação em puérperas adolescentes e
adultas jovens da Região Nordeste do Brasil: análise múltipla em modelos hierárquicos.
Revista Brasileira de Saúde Materno Infantil.2017;17(4):673–81.
31. Love ER, Crum J, Bhattacharya S. Independent effects of pregnancy induced hypertension
on childhood development: a retrospective cohort study. Eur J Obstet Gynecol Reprod Biol.
2012;165(2):219–24.
32. Rezende KBC, Bornia RG, Esteves APVS, Cunha AJL, Junior JA et al. Preeclampsia:
Prevalence and perinatal repercussions in a University Hospital in Rio de Janeiro, Brazil.
Pregnancy Hypertension: An International Journal of Women’s.2016; 6(4) 253-55.
33. Johnson KM, Zash R, Haviland MJ, Hacker MR, Luckett R, Diseko M et al. Hypertensive
disease in pregnancy in Botswana: prevalence and impact on perinatal outcomes. Pregnancy
Hypertens. 2016;6(4):418–22.
34. Werneck GL. Diagramas causais: a epidemiologia brasileira de volta para o futuro. Cad.
Saúde Pública. 2016; 32(8): e00120416.

19
35. Jacob LMDS, Santos AP, Lopes M HB M, Shimo AKK. Socioeconomic, demographic
and obstetric profile of pregnant women with Hypertensive Syndrome in a public maternity.
Revista Gaúcha de Enfermagem. 2020; 41, p. e20190180.
36. Vogel JP, Chawanpaiboon S, Moller AB, Watananirun K, Bonet M, Lumbiganon P. The
global epidemiology of preterm birth. Best Practice & Research Clinical Obstetrics &
Gynaecology. 2018;52; 3-12.
37. Kahsay HB, Gashe FE, Ayele WM. Risk factors for hypertensive disorders of pregnancy
among mothers in Tigray region, Ethiopia: matched case-control study. BMC Pregnancy and
Childbirth. 2018; 18, 482.
38. Billionnet C, Mitanchez D, Weill A, Nizard J, Alla F, Hartemann A, et al. Gestational
diabetes and adverse perinatal outcomes from 716,152 births in France in 2012. Diabetologia.
2017;60, 636-44.
39. Cavalli RDC, Sandrim VC, Santos JETD, Duarte G. Predição de pré-eclâmpsia. Revista
Brasileira de Ginecologia e Obstetrícia. 2009, 31; 1-4.
40. Sbrana M, Grandi C, Brazan M, Junquera N, Nascimento MS, Barbieri MA, Bettiol H,
Cardoso VC. Alcohol consumption during pregnancy and perinatal results: a cohort study.
Sao Paulo Med J. 2016; 134 (2): 146-52
41. Hinkosa L, Tamene A, Gebeyehu N. Risk factors associated with hypertensive disorders
in pregnancy in Nekemte referral hospital, from July 2015 to June 2017, Ethiopia: case-
control study. BMC Pregnancy and Childbirth. 2020; 20; 1-9.
42. Pervin J, Rahman SM, Rahman M, Aktar S, Rahman A. Association between antenatal
care visit and preterm birth: a cohort study in rural Bangladesh. BMJ Open. 2020; 10(7),
e036699.
43. Shen M, Smith GN, Rodger M, White RR, Walker MC, Wen SW. Comparison of risk
factors and outcomes of gestational hypertension and pre-eclampsia. PloS one. 201; 12(4),
e0175914.
44. Lefizelier E, Misbert E, Brooks M, Le Thuaut A, Winer N, Ducarme G. Preterm birth and
small-for-gestational age neonates among pre pregnancy underweight women: a case-
controlled study. Journal of Clinical Medicine. 2021; 10(24), 5733.
45. Austin PC. An Introduction to Propensity Score Methods for Reducing the Effects of
Confounding in Observational Studies. Multivariate Behav Res. 2011; 46(3): 399–424.

20
46. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational
studies for causal effects. Biometrika. 1983;70(1):41-55.
47. Leite W. Practical propensity score methods using R. Los Angeles: Sage, 2017. cap 6 -
Propensity score methods for multiple treatments.
48. Arce-López, KL, Iglesias-Leboreiro J, Bernárdez-Zapata I, Rendón-Macías ME, Madrazo
M. Hematological disorders in preterm newborns born to mothers with pregnancy-induced
hypertension. Bolet médico del Hospital Infantil de México. 2022; 79(6); 363-68, 2022
49. Cruz SS, Costa VS, Santos JM, Gomes TAO, Brandão ITR, Fonseca CHA et al.
Prematuridade ao nascer, hipertensão materna e outros fatores associados: um estudo de caso-
controle na região do Vale do São Francisco. Revista Brasileira de Pesquisa em Saúde. 2018;
20(4), 113-19.
50. Purisch SE, Gyamfi-Bannerman C. Epidemiology of preterm birth. Semin Perinatol
2017;41(7):387–91.
51. Bajaj M, Natarajan G, Shankaran S, Wycokoff M, Laptoock AR, Bell EF et al. Delivery
Room Resuscitation and Short-term Outcomes in Moderately Preterm Infants. J Pediatr 2018;
195:33-38. e2.
52. Boyle EM, Johnson S, Manktelow B, Seaton SE, Draper SE, Smith KL et al. Neonatal
outcomes and delivery of care for infants born late preterm or moderately preterm: a
prospective population-based study. Archives of Disease in Childhood - Fetal and Neonatal
Edition 2015;100(6): F479–F485.
53. Almeida AHV, Gama SGN, Costa MCO, Carmo CN, Pacheco VE, Martinelli KG, et al.
Prematuridade e gravidez na adolescência no Brasil, 2011-2012. Cadernos de Saúde Pública.
2020, 36.

21

Figure 1: Flowchart of the number of early and late premature newborns
Total number of newborns in the study
“Born in Brazil”
n= 24,200

Premature and full-term births
n= 20,514

Premature and full-term births
n= 20,514

Excluded:

 489 newborn twins
 61 newborns whose mothers did not have information on
hypertensive syndromes during pregnancy
 933 newborns whose mothers did not have information on the
adequacy of prenatal care:
 2 newborns whose mothers did not have information on age
 1 newborn whose mother did not have information on parity
 2,200 newborns (≥41 weeks)

Excluded:
Early term newborns
(37 to 38 weeks)
n= 8,336

Premature births
(<37 weeks)
n= 2,139

Full-term births
(39 to 40 weeks)
n=10,039

Early premature births
(<34 weeks)
n=534

Late Preterm Births
(34 to 36 weeks)
n=1,605

(<33 semanas)
n= 534

22

Figure 2: Directed acyclic graph between hypertensive syndromes of pregnancy and prematurity

HSP: hypertensive syndromes during pregnancy Type of birth (cesarean section and vaginal delivery)
Pre-existing diseases (autoimmune disease, chronic
kidney disease)

Congenital anomalies (myoma and short cervical
length)

Previous infectious diseases (HIV and syphilis) BMI: Body Mass Index
IUGR: intrauterine growth restriction IPV: intimate partner violence
Behavioral habits (use of illicit drugs, alcoholism,
smoking)
Socioeconomic level (education)
Table 1. Maternal, prenatal, and birth characteristics according to hypertensive syndromes during pregnancy. Brazil, 2011-2012

Hypertensive syndromes during pregnancy

Variables used in weighting

No
(18,125)
n (%)

Yes
(2.369)
n (%) p-value b
Maternal age (years) 0.038
12-19 3,346 (18.5) 478 (20.2)
20-34 12,795 (70.6) 1.662 (70.2)
≥ 35 1,984 (10.9) 229 (9.7)
Maternal education 0.117
Incomplete elementary school 4,652 (25.7) 653 (27.6)
Complete elementary school 4,432 (24.5) 559 (23.6)
Complete high school 7,051 (38.9) 923 (39.0)
High school and higher education 1,990 (11.0) 234 (9.9)
Marital status 0.178
Without a partner 3,107 (17.1) 433 (18.3)
With a partner 15,018 (82.9) 19.36 (81.7)

23
Pre-gestational BMI (kg/m2) 0.421
Low weight (≤18.5) 1,489 (8.2) 207(8.7)
Eutrophic (18.5-24.9) 10,848 (59.8) 1.412 (59.6)
Overweight (25–29.9) 4,128 (22.8) 553 (23.3)
Obesity (≥30) 1,668 (9.2) 197(8.3)
Gestational weight gain 0.927
Insufficient 4,949 (27.3) 643 (27.1)
Adequate 5,830 (32.2) 756 (31.9)
Excessive 7,346 (40.5) 970 (40.9)
Adequate prenatal care 0.213
Inadequate 3,451(19.0) 490 (20.7)
Partially adequate 4,760 (26.3) 604 (25.5)
Adequate 5,772 (31.8) 759 (32.0)
More than adequate 4,142 (22.9) 516 (21.8)
Parity 0.627
Primiparous 8,485 (46.8) 1,126 (47.5)
1 to 2 births 7,798 (43.0) 995 (42.0)
3 or more births 1,842 (10.2) 248 (10.5)
Maternal anemia 0.501
No 17,706 (97.7) 2,320 (97.9)
Yes 419 (2.3) 49 (2.1)
Pre-gestational diabetes mellitus 0.453
No 17,959 (99.1) 2,543 (98.9)
Yes 166 (0.9) 26 (1.1)
Gestational diabetes mellitus 0.035
No 16,648 (91.9) 2,206 (93.1)
Yes 1477 (8.1) 163 (6.9)
Autoimmune disease (SLE) 1.00
No 18,084 (99.8) 2,364 (99.8)
Yes 41 (0.2) 5 (0.2)
Chronic kidney disease 0.542
No 18,096 (99.8) 2,367 (99.9)
Yes 29 (0.2) 2 (0.1)
Urinary tract infection 0.215
No 15,689 (86.6) 2.073 (87.5)
Yes 2,436 (13.4) 296 (12.5)
a All variables were selected based on the directed acyclic graph
b Rao-Scott chi-square test; SLE: systemic lupus erythematosus
BMI: body mass index; kg: kilogram; m 2 : square meters

24

Table 2. Maternal, prenatal, and birth characteristics according to early and late preterm birth. Brazil, 2011-2012
Variables used in weighting 39-40 gestational
weeks
(n=10,039)
n (%)

Early premature
birth b
(n=534)
n (%)

p-value c

Late premature
birth b
(n=1.605)
n (%)

p-value c
Maternal age (years) <0,001 <0,001
12-19 1,577 (15.7) 386 (24.0)
20-34 7,234 (72.1) 353 (66.1) 1.095 (68.2)
≥ 35 1,228 (12.2) 35 (6.6) 124 (7.7)
Maternal education 146 (27.3, <0.001 <0.001
Incomplete elementary school 1,901 (18.9) 206 (38.6) 495 (30.8)
Complete elementary school 2,457 (24.5) 122 (22.8) 422 (26.3)
Complete high school 4,370 (43.5) 159 (29.8) 586 (36.5)
Higher education and more 1,311 (13.1) 47 (8.8) 102 (6.4)
Marital status 0.090 <0.001
No partner 1,621 (16.1) 71 (13.3) 333 (20.7)
With partner 8,418 (83.9) 463 (86.7) 1.272 (79.3)
Pre-gestational BMI (kg/m2) 0.0126 <0.001
Underweight (≤18.5) 720 (7.2) 53 (9.9) 164 (10.2)
Eutrophic (18.5-24.9) 5,887 (58.6) 322 (60.2) 974 (60.7)
Overweight (25–29.9) 2,377 (23.7) 120 (22.5) 361 (22.5)
Obesity (≥30) 1,055 (10.5) 39 (7.3) 106 (6.6)
Gestational weight gain 0.285 0.028
Insufficient 2,551 (25.4) 145 (27.2) 458 (28.5)
Adequate 3,211 (32.0) 180 (33.7) 498 (31.0)
Excessive 4,277 (42.6) 209 (39.1) 649 (40.4)
Adequate prenatal care <0.001 <0.001
Inadequate 1,459 (14.5) 166 (31.1) 496 (30.9)
Partially adequate 2,553 (25.4) 133 (24.9) 430 (26.8)
Adequate 3,076 (30.6) 160 (30.0) 443 (27.6)
More than adequate 2,951 (29.4) 75 (14.0) 236 (14.7)
Parity <0.001 <0.001
Primiparous 4,708 (46.9) 222 (41.6) 684 (42.6)
1 to 2 births 4,478 (44.6) 222 (41.6) 688 (42.9)
3 or more births 853 (8.5) 90 (16.9) 233 (14.5)
Maternal anemia 0.458 0.177
No 9,799 (97.6) 518 (97.0) 1.557 (97.0)
Yes 240 (2.4) 16 (3.0) 48 (3.0)
Pre-gestational diabetes mellitus 0.171 0.108
No 9,929 (98.9) 532 (99.6) 1.595 (99.4)
Yes 110 (1.1) 02 (0.4) 10 (0.6)
Gestational diabetes mellitus 0.0019 <0.001
No 9,059 (90.2) 504 (94.4) 1.520 (94.7)
Yes 980 (9.8) 30 (5.6) 85 (5.3)
Autoimmune disease (SLE) 0.976 0.963
No 10,012 (99.7) 532 (99.6) 1.600 (99.7)
Yes 27 (0.3) 02 (0.4) 05 (0.3)
Chronic kidney disease 0.952 -
No 10,019 (99.8) 533 (99.8) 1.605 (100)
Yes 20 (0.2) 01 (0.2) 0 (0.0)
Urinary tract infection 0.002 <0.001
No 8,466 (84.3) 423 (79.2) 1.459 (90.9)
Yes 1,573 (15.7) 111 (20.8) 146 (9.1)
a All variables were selected based on the directed acyclic graph
b The outcomes were compared with the category: 39-40 gestational weeks
C Rao-Scott Chi-square test
BMI: Body mass index; kg: kilogram; m 2 : square meters; SLE: systemic lupus erythematosus

25
Table 3. Logistic regression between the variables of the minimum adjustment set and early and late preterm
birth. Brazil, 2011-2012
Variables used in weighting a Early preterm birth b
(n=534)

Late preterm birth b
(n=1,605)

Maternal age (years)
12-19 1.90 (1.55 - 2.32) 1.62 (1.42 - 1.84)
20-34 1.00 1.00
≥ 35 0.58 (0.41 - 0.83) 0.35 (0.55 - 0.81)
Maternal education
Incomplete elementary school 3.02 (2.18 - 4.18) 3.35 (2.67 - 4.19)
Complete elementary school 1.39 (0.98 - 1.95) 2.21 (1.76 - 2.77)
Complete high school 1.01 (0.73 - 1.41) 1.72 (1.38 - 2.15)
High school and higher education 1.00 1.00
Marital status
Without a partner 1.00 1.00
With a partner 1.26 (0.97-1.62) 1.36 (1.91 – 1.55)
Pre-gestational BMI (kg/m2)
Underweight (≤18.5) 1.35 (1.10-1.82) 1.38 (1.15 - 1.65)
Eutrophic (18.5-24.9) 1.00 1.00
Overweight (25–29.9) 0.92 (0.74-1.14) 0.92 (0.81 - 1.05)
Obesity (≥30) 0.68 (0.48-0.95) 0.61 (0.49 - 0.75)
Gestational weight gain
Insufficient 1.01 (0.81 - 1.27) 0.98 (0.86 - 1.11)
Adequate 1.00 1.00
Excessive 0.87 (0.71-1.07) 1.16 (1.01 - 1.33)
Adequate prenatal care
Inadequate 2.19 (1.75-2.74) 2.36 (2.05-2.72)
Partially adequate 1.00 (0.79-1.27) 1.17 (1.01-1.35)
Adequate 1.00 1.00
More than adequate 0.49 (0.37-0.65) 0.56 (0.47-0.66)
Parity
Primiparous 1.00 1.00
1 to 2 births 1.05 (0.87-1.27) 1.06 (0.94 - 1.18)
3 or more births 2.24 (1.73-2.89) 1.88 (1.59 – 2.22)
Maternal anemia
No 1.00 1.00
Yes 1.26 (0.75-2.11) 1.26 (0.92-1.72)
Pre-gestational diabetes mellitus
No 1.00 1.00
Yes 0.34 (0.08-1.38) 0.57 (0.30 -1.08)
Gestational diabetes mellitus
No 1.00 1.00
Yes 0.55 (0.38-0.80) 0.52 (0.41 - 0.65)
Autoimmune disease (SLE)
No 1.00 1.00
Yes 1.39 (0.33 – 5.88) 1.16 (0.45 -3.01)
Chronic kidney disease
No 1.00 1.00
Yes 0.94 (0.13-7.02) -
Urinary tract infection
No 1.00 1.00
Yes 1.41 (1.14-1.75) 0.54 (0.45 – 0.64)
a All variables were selected based on the directed acyclic graph
b The outcomes were compared with the category: 39-40 gestational weeks
C Rao-Scott Chi-square test
BMI: Body mass index; kg: kilogram; m 2 : square meters; SLE: systemic lupus erythematosus

26

Table 4. Crude and adjusted odds ratios for early and late preterm birth. Brazil 2011-2012

Early preterm birth Late preterm birth
Crude OR
(95% CI)

Adjusted OR*
(95% CI)

Crude OR
(95% CI)

Adjusted OR*
(95% CI)

Hypertensive Syndrome during
Pregnancy (HSP)
No 1.0 1.00 1.0 1.00
Yes 3.34 (2.72-4.10) 2.74 (2.12-3.54) 2.41 (2.09-2.77) 2.40 (1.86-3.08)
* Adjusted for weighting. Early prematurity: (< 34 weeks); Late prematurity (34 to 36 weeks); OR: odds ratio; 95% CI:
95% confidence interval. Minimum adjustment variables: maternal age, maternal education, marital status, pre-gestational
BMI, gestational weight gain, adequacy of prenatal care, parity, anemia, pre-gestational diabetes mellitus, gestational
diabetes mellitus, autoimmune disease (systemic lupus erythematosus, chronic kidney disease, urinary tract infection).