ADVANCED MATERNAL AGE AND PLACENTA PREVIA FOR WOMEN

GIVING BIRTH IN FINLAND; A REGISTER-BASED COHORT STUDY

Zahra Roustaei Master's thesis Public Health School of Medicine Faculty of Health Sciences

University of Eastern Finland

May 2017

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UNIVERSITY OF EASTERN FINLAND, Faculty of Health Sciences

Public health ZAHRA R.: Advanced maternal age and placenta previa for women giving birth in Finland; a

register-based cohort study

Master's thesis, 60 pages. Instructors: Professor Katri Vehviläinen-Julkunen, Ph.D and professor Tomi-Pekka Tuomainen,

MD, PhD. May 2017

Key words: Advanced maternal age, placenta previa, maternal outcome, neonatal outcome

ADVANCED MATERNAL AGE AND PLACENTA PREVIA FOR WOMEN GIVING BIRTH

IN FINLAND; A REGISTER-BASED COHORT STUDY

Increasing maternal age at the time of delivery and the prevalence of placenta previa provided

more implications in practice. Thus, the aim of this study was to assess the association of Advanced

Maternal Age (AMA) with placenta previa and to explore the effect of AMA on maternal and

neonatal outcomes of placenta previa.

This study was a register-based cohort study, using data of three Finnish health registries from

2004 to 2008, including information of 283 324 women and their newborns. The association

between AMA and placenta previa was modelled using a backwards, stepwise logistic regression.

Multivariable logistic regression modelling was used to assess the effect of maternal age 35 years

or older on maternal and neonatal outcomes of placenta previa. The main outcome measures were

blood transfusion, placental abruption, preterm birth <37 weeks, Neonatal Intensive Care Unit

(NICU) admission, low birth weight <2,500 g and low Apgar score at 5 minutes.

A total of 283 324 deliveries, of which 714 (0.3%) were complicated by placenta previa. AMA

was an independent risk factor for placenta prevai, Adjusted Odds Ratio (AOR) 1.54; 95%

Confidence Interval (CI) (1.30-1.83). Adverse maternal and neonatal outcomes generally increased

in women with placenta previa, with different patterns across age groups. By considering women

without placenta previa as reference groups, the AOR 95 % CI in AMA and young women with

previa were 7.3 (5.0-10.6) and 6.8 (5.2-8.9) in blood transfusion, 11.3 (5.4-23.3) and 10.9 (6.1-

19.6) in placental abruption. In neonatal outcomes the AOR and 95% CI in AMA and young

women with placenta previa were 8.8 (6.6-11.6) and 11.7 (9.7-14.1) in preterm birth, 4.0 (3.0-5.3)

and 4.9 (4.1-5.9) in NICU admission, 4.0 (2.8-5.7) and 5.9(4.7-7.4) in low birth weight, 2.7 (1.5-

4.9) and 3.3 (2.2-5.0) in low Apgar score at 5 minutes.

The results demonstrated that AMA was an independent risk factor for placenta previa. AMA

women with placenta previa had slightly higher adjusted risks of blood transfusion and placental

abruption than younger women with placenta previa, but not greater risks of neonatal outcomes.

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ACKNOWLEDGMENTS

The present study was carried out at Department of Public Health, Faculty of Health Sciences

University of Eastern Finland. The second aim of this thesis was based on the manuscript with the

topic of 'The effect of advanced maternal age on placenta previa outcomes: a register-based cohort

study’ which is currently under review in a journal.

I would like to thank Professor Katri Vehviläinen-Julkunen, PhD, for supervising my thesis and

giving guidance. I would like to thank Professor Tomi Pekka-Tumainen, MD, PhD for supervising

me and giving continuous support. I would like to thank Professor Seppo Heinonen, MD, PhD, for

his time, knowledge and guidance. I would like to thank Reeta Lamminpää, PhD, for giving all

help and prompt answers to my questions. I would like to thank Ari Voutilainen, PhD, for his kind

assistance in plotting figures and giving statistical guidance. I would like to thank The National

Institute for Health and Welfare for giving me the possibility to use register-based data. I owe my

deepest gratitude to all of them for their support.

Kuopio

May 2017

Zahra Roustaei

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CONTENTS

1 ABBREVIATIONS ............................................................................................................... 5

2 INTRODUCTION................................................................................................................. 6

3 LITERATURE REVIEW .................................................................................................... 7

3.1 Advanced Maternal Age ................................................................................................ 7

3.1.1 Definition of Advanced Maternal Age (AMA) ..................................................... 7

3.1.2 Postponing childbirth ............................................................................................. 8

3.1.3 Factors associated with postponing childbirth ..................................................... 9

3.1.4 Postponing childbirth in Finland ......................................................................... 10

3.1.5 The causal effect of AMA and adverse pregnancy outcomes ........................... 11

3.1.6 AMA and placenta previa .................................................................................... 13

3.2 Placenta previa ............................................................................................................. 14

3.2.1 General aspects...................................................................................................... 14

3.2.2 Etiology and risk factors of placenta previa ....................................................... 14

3.2.3 Effect of placenta previa on maternal outcomes ................................................ 16

3.2.4 Effete of placenta previe on neonatal outcomes. ................................................ 18

3.2.5 Effect of risk factors on placenta previa outcomes ............................................ 19

3.3 Register-based study .................................................................................................... 19

3.3.1 Register-based data in pregnancy research ........................................................ 19

3.3.2 Strengths and limitations of register-based research ........................................ 20

4 AIMS OF THE STUDY...................................................................................................... 24

5 METHODS .......................................................................................................................... 25

5.1 Study population .......................................................................................................... 25

5.1.1 Medical Birth Register (MBR) ............................................................................ 25

5.1.2 Hospital Discharge Register (HDR) .................................................................... 25

5.1.3 Register of Congenital Malformation (RCM) .................................................... 25

5.2 Variables and definitions ............................................................................................. 26

5.2.1 Independent variables .......................................................................................... 26

5.2.2 Dependent variables.............................................................................................. 26

5.2.3 Confounding variables.......................................................................................... 27

5.3 Design of the study ....................................................................................................... 27

5.4 Statistical analysis ........................................................................................................ 28

5.5 Ethical considerations .................................................................................................. 28

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6 RESULTS ............................................................................................................................ 29

6.1 Characteristics of the studied population .................................................................. 29

6.2 Effect of AMA on placenta previa .............................................................................. 31

6.3 Unadjusted effect of maternal age on placenta previa outcomes............................. 31

6.4 Adjusted effect of maternal age on placenta previa outcomes ................................. 34

7 DISCUSSIONS .................................................................................................................... 36

7.1 Interpretation of the results ........................................................................................ 36

7.2 Strengths and weaknesses ............................................................................................ 39

7.3 Generalizability of the results ..................................................................................... 39

7.4 Implications for practice .............................................................................................. 40

7.5 Implications for research ............................................................................................. 40

8 CONCLUSIONS ................................................................................................................. 42

9 REFRENCES ...................................................................................................................... 43

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1 ABBREVIATIONS

AMA Advanced Maternal Age

AOR Adjusted Odds Ratio

ART Assisted Reproductive Technology

CI Confidence Interval

ICD International Classification of Diseases

IUI Intrauterine Insemination

IVF In-Vitro Fertilization

LBW Low Birth Weight

MBR Medical Birth Register

NICU Neonatal Intensive Care Unit

SGA Small for Gestational Age

THL The National Institute for Health and Welfare

US United States

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2 INTRODUCTION

Postponement of motherhood has become the main characteristic of fertility patterns in many

developed countries (Mathews & Hamilton 2009). In the last decades, the trend toward delaying

motherhood and the shift in age demographic were so prominent that Goldstein and associates

(2009) used the term ‘postponement transition’ as a change from the younger to the older age of

motherhood. In Finland, the average age of women at first child increased from 26.5 in 1987 to

28.8 in 2015 (Klemetti et al. 2016, THL 2017). The process of postponement transition has

negatively affected the demographic situation and overall fertility rates in many developed

countries (Billari & Kohler 2004). Although, women are aware of associated risks of postponing

motherhood, they postpone childbearing for many reasons, ranging from individual, family and

social to national and international factors (Maheshwari et al. 2008, Fagbamigbe & Idemudia

2016). Advanced Maternal Age (AMA) is mainly determined as maternal age more than 35 and

there is a growing body of evidence that AMA is associated with adverse pregnancy outcomes

(Usta & Nassar 2008, Bayrampour & Heaman 2010, Kenny et al. 2013).

The prevalence of placenta previa was estimated to be 5.2 per 1000 pregnancies (Cresswell et al.

2013). Placenta previa is an obstetric complication in which the internal cervical os is partially or

completely covered with placental and is a leading cause of abnormal antepartum bleeding

(Cunningham et al. 2010). The risk of having placenta previa in pregnancies is increasing as a

result of changing trends in risk factors such as increased rates of Caesarean section and

introduction of In-Vitro Fertilization (IVF) in fertility treatment ((Nørgaard et al. 2012). Studies

indicated that placenta previa is significantly associated with adverse maternal and neonatal

outcomes (Crane et al. 2000, Rosenberg et al. 2011, Mastrolia et al. 2016).

Despite a large volume of literatures in the area of advanced prenatal outcomes of AMA and

placenta previa, the association between AMA and the risk of placenta previa was not consistent

across studies. Furthermore, we hypnotized that the effect of poor placenta bed in placenta previa

compound with uteroplacental under perfusion in AMA may worsen pregnancy outcomes.

However, evidence supporting the effect of individual-level risk factors such as AMA on placenta

previa outcomes is lacking. Thus, population-based data were used to assess the association of

AMA with placenta previa and to explore the effect of AMA on perinatal outcomes of placenta

previa, in terms of magnitude of the adverse outcomes from younger women with placenta previa.

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3 LITERATURE REVIEW

3.1 Advanced Maternal Age

3.1.1 Definition of Advanced Maternal Age (AMA)

It has always been arguments about the optimal timing to be a mother, but there is no consensus

on the exact definition of AMA. In social sciences, AMA is considered as the normative

acceptability of older parenthood in the society (Boivin et al. 2009). In the majority of European

countries, the age at which is considered too late to get pregnant is over the age of 40 (Mills et al.

2011). From a medical point of view, AMA is usually defined based on pregnancy outcomes that

affect the wellbeing of mothers. In today's world, postponing motherhood is more acceptable and

sometimes it is considered to be beneficial for women, because of bringing sociological advantages

over biological disadvantages (Stein & Susser 2000). From a social perspective, older mothers

have more stable sources of income, higher levels of education and more life experience (Nilsen

et al. 2012). From a medical perspective, AMA women have more pregnancy-related

complications than younger women (Laopaiboon et al. 2014).

In assessing the effect of maternal age on pregnancy outcomes, it is necessary to draw a boundary

between the low-risk and high- risk pregnancies. Studies reported different boundaries for defining

the adverse effect of maternal age on pregnancy outcomes. However, AMA is commonly defined

as maternal age 35 or older and majority of studies used this cut-off age to evaluate adverse

pregnancy outcomes (Usta & Nassar 2008, Mills & Lavender 2010).

Some studies considered the cut-off age of 30 to assess whether there was an increased risk before

the cut of age of 35 years, while in other studies a cut-off age was considered more than 40 or 45

(Blomberg et al. 2014, Grotegut et al. 2014, Waldenström et al. 2014). The study of Klemetti and

associates (2016) reported precise threshold-ages for adverse pregnancy outcomes by considering

maternal age as a continuous variable, without defining any specific boundaries. However, the

study of Cleary-Goldman and associates (2005) suggested that maternal age has a continuum effect

rather than a threshold effect.

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The exact definition of AMA is hard to define, because the risk of unfavorable pregnancy outcomes

may occur earlier or later than a 35 years cut-off age (Lamminpää 2015). However, this thesis may

contribute more to the exact definition of AMA by focusing on the medical aspects of age-related

complications.

3.1.2 Postponing childbirth

Postponement of childbearing has been linked to the demographic transition in many developed

countries (Lesthaeghe & Neels 2002). The phenomenon of demographic transition was defined as

the transition from high birth to low birth rates and begun in the beginning of the 19th century in

Europe, known as ‘the first demographic transition’. The first transition was mainly influenced by

concerns for family and offspring, characterized by urbanization, industrialization and

secularization (Van de Kaa 1987, Lesthaeghe 2014). In the first transition, economic utilization

of children was reduced and parents had to invest more on children to give them a better chance

of future life. At the same time, the influence of churches was reduced by secularization and use

of birth control device increased (Van de Kaa 1987). The second transition happened in the 1960s

and moved Europeans from marriage and parenthood toward individualism with increasing rates

of divorce and premarital cohabitation in postindustrial societies (Bianchi 2014). During this

period, men and women tended to have a decent income and a deliberate choice of bringing a child

for women meant to leave their job. Beyond economic reasons, social and cultural factors played

a substantial role in postponing marriage and parenthood in postindustrial societies. The second

transition emphasized on the fact that how changes in values and attitudes can play roles in

postponing parenthood, while in the first transition the main reason for demographic transition was

value of children (Bianchi 2014).

In recent societies, the increase in the mean maternal age at the time of delivery has become an

important characteristic of women in many developed and some developing countries. The recent

worldwide trends in postponing pregnancy and decreasing fertility rates were so tangible that some

studies reported AMA as a public health issue and French High Council for Population and Family

recommended to consider AMA as a public health agenda (Lemoine & Ravitsky 2015). The shift

in average maternal age toward later motherhood has been reported in many developed countries.

The mean maternal age at delivery of the first child increased almost 5 years in Denmark from

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1978 to 2007 (Nørgaard et al. 2012). The rise in maternal age has been observed in the United

States (US); the mean maternal age increased in all race and Hispanic origin groups, from 24.9 in

2000 to 26.3 in 2014 (Mathews & Hamilton 2016). Canada had an almost 3-fold increase in the

proportion of AMA women from 1987 to 2005 (Lemoine & Ravitsky 2015). In Australia, the

median age of mothers increased from 25.4 years in 1971 to 29.3 years in 2013 (Allison et al.

2016). On the other hand, similar trends have been observed in some developing countries. In Iran,

many factors have contributed to decline in fertility rates from 6.9 in 1980 to 1.6 in 2011, of which

31% was due to the postponement of marriage among young adults (Saadat et al. 2010, Erfani

2014).

3.1.3 Factors associated with postponing childbirth

The underlying reasons for postponing childbearing are divers in different time periods. In the first

transition, economic factors and changes in child labour attitudes were the main reasons for

postponing parenthood (Van de Kaa 1987). Given the recent trends in childbearing age, the

tendency towards postponing childbirth results from a myriad of factors, ranging from individual,

family and social factors to national and international factors (Fagbamigbe & Idemudia 2016).

In recent decades, social advance toward women empowerment provided women with an

opportunity to pursue education and career goals before having a family (Hadfield 2007).

Acquiring higher levels of education usually is time consuming and balancing the role of

motherhood with educational attainment is hard to achieve. Therefore, women prefer to postpone

motherhood to the later time. Furthermore, well-educated women tend to have serious careers

which need more time, responsibility and autonomy (Mills et al. 2011). Thus, women prefer to

postpone motherhood until the time they reach stability in their career.

Another underlying reason for delaying childbearing is that nowadays women have a greater

control over their pregnancy, thanks to widespread use of contraceptives and the possibility of

legal abortion in some countries (Bailey 2006). Therefore, women are able to manipulate the

timing of reproduction and allocate less time to the maternal role. Additionally, the introduction

of Assisted Reproductive Technology (ART) in the 1970s gave the notion that pregnancy is

possible at even older age, without knowing the fact that the success rate of ART is highly related

to the age of mothers (Simpson 1998, Leridon 2004). Moreover, the transition to motherhood can

be inhibited by physical wellbeing, emotional conditions, stressful environment and lack of

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knowledge (Meleis et al. 2000). On the other hand, in some situations postponing motherhood is

not an individual choice and is mainly guided by external factors. Study of Abu Heija and

associates (2000) reported that women in Jordan continue to bear children until the end of

childbearing years, because of social and religious reasons. Housing and economic uncertainly and

lack of family support policies as well as a departure from traditional values, norm and beliefs are

another determinants of postponing childbearing (Mills et al. 2011).

3.1.4 Postponing childbirth in Finland

Like other developed countries, in Finland, there is a tendency toward postponement of

childbearing and Finnish birth cohorts confirmed this trend. In Finland, the proportion of

primiparous mothers aged 40 or older has increased from 12% in 1987 to 20% in 2010 (Klemetti

et al. 2013). Finnish mothers had the mean maternal age of 26.5 in 1987 and 28.8 in 2015, with an

almost 2- year increased (Klemetti et al. 2016, THL 2017). The results of a comparative research

showed that irrespective of all similarities in fertility patterns among the Nordic countries, Finnish

women had the highest mean age and Norwegian women had the lowest mean age at birth. In

Finnish women, the process of postponement of childbearing started in the 1945−49 cohort, sooner

than the other Nordic countries, but this postponement transition has not ground to a halt

(Andersson et al. 2009).

In Nordic countries gender equality policies encouraged women to participate actively in the

society. Consequently, the proportion of women with university degrees has substantially

increased and among all Nordic countries, Finland has had the highest proportion of women with

higher education. This preference of Finnish women in taking educational opportunities was linked

to the highest median age of Finnish mothers at the time of delivery among Nordic countries.

However, higher level of education in Finnish mothers was not associated with a lower number of

children (Andersson et al. 2009). This effect may be modified by the Finnish family policy and

paid parental leave that try to keep the birth over the replacement level of developed countries so

that women preferred to take the advantages of unemployment for a while (Vikat 2004, Eurostat

2017). Thus, in Finnish mother, AMA is associated with higher levels of education, later transition

to motherhood, but not the lower number of children. This inverse relationship between levels of

education and the timing of the first child has been documented in another study (Martin 2000).

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From a biological perspective, older women tend to marry with older men and this can contribute

to subfertility or in fecundability. Although the ability to conceive is less affected by age in men,

in AMA women the biological ability to conceive decreases with advancing age (Dunson et al.

2002). Thus, postponing pregnancy to the older age may affect to some extent, the total fertility

rate in Finland and it has contributed to the rate of fertility below the recommended replacement

level for developed countries (Regushevskaya et al. 2013). Along with increase in maternal age

and decline in fertility rate, the use of ART has increased in Finland and 4.2 of newborns were

artificially convicted in 2014 (THL 2015a). However, use of ART cannot entirely compensate the

decline in fertility by age (Schmidt et al. 2012).

One study in the US reported that AMA is associated with a higher risk of maternal mortality, but

recent advances in technology and medical care has ameliorated the risk of maternal mortality

(Buehler et al. 1986). However, Klemettie and associates (2013) reported that despite all changes

and advances in maternity care in Finland, older mothers use more antenatal services than the

younger counterpart, which stems from the fact that AMA women and their infants have more

health problems. Although, AMA is associated with more use of antenatal services and higher

rates of interventions, these associations cannot be entirely explained by higher rates of pregnancy

complications in this age group. One study in the United Kingdom reported that, higher rates of

interventions in older women reflect AMA as a real phenomenon and it cannot be entirely

explained by more obstetric complications (Bell et al. 2001).

3.1.5 The causal effect of AMA and adverse pregnancy outcomes

Numerous studies have reported that AMA is associated with multiple adverse pregnancy

outcomes. Maternal and neonatal complications, including chromosomal abnormalities, congenital

anomalies, low birth weight, preterm birth, diabetes, high blood pressure, preeclampsia, breech

presentation, operative vaginal delivery, emergency Caesarean section, postpartum haemorrhage,

birth weight below the 5th percentile, stillbirth and placenta previa (Jolly et al. 2000, Delbaere et

al. 2007, Flenady et al. 2011, Laopaiboon et al. 2014). A study of 34,695 pregnant women with

singleton pregnancy found higher rates of pre-existing medical conditions such as diabetes and

high blood pressure in advanced maternal age women in compared to women aged 25 to 29 years

(Ludford et al. 2012). Grotegut and associates (2014) reported that the number of women giving

birth in 45 years and more is increasing and found that these women had increased risks of maternal

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death. In addition to that aggregated results of 38 countries revealed that maternal mortality has a

"J" shaped curve and the risk increased after age 30 (Blanc et al. 2013). Therefore, the results of

these studies indicated that AMA contributes to multiple adverse effects rather than a single effect

on pregnancy. In other words, AMA not only increases the risk of age-related complications such

diabetes or hypertensions, it is associated with higher risks of many other maternal and neonatal

adverse outcomes.

Studies used different effect measures to evaluate the association of AMA with adverse pregnancy

outcomes, but most of the studies reported that there is a strong association between AMA and

pregnancy outcomes. Even after controlling for potential confounding factors, the strength of

association between AMA and pregnancy outcomes persists (Cleary-Goldman et al. 2005, Biro et

al. 2012). The existence of statistically significant associations in the majority of studies illustrates

more on the importance of AMA as a risk factor for many pregnancy complications and indicates

that AMA quantitatively and clinically is connected to adverse outcomes in pregnancies.

Additionally, the effect of AMA on pregnancy outcomes may be worsened by the effect of

different risk factors. For example, based on “weathering” hypothesis, the risk of preterm birth in

older African Americans was greater than white Americans mothers (Holzman et al. 2009).

According to “weathering” hypothesis, the effect of health inequality coupled with AMA, widened

the gap between adverse pregnancy outcomes in African Americans and white Americans. Another

study reported that the adverse effect of smoking on the risk of preterm birth intensifies in AMA

women. It means that older mothers are more affected by the detrimental effect of smoking than

younger mothers (Bonellie 2001). Thus, in many circumstances, older women had more

unfavorable pregnancy outcomes than younger counterparts and various factors such as health

inequity and smoking may lead to increased risks of unfavorable pregnancy outcomes.

In some studies, maternal age was categorized in different categories to assess different levels of

exposure of risk factor on pregnancy outcomes. In the study of Jolly and associates (2000) was

reported that the odds of having gestational diabetes, breech presentation, operative vaginal

delivery, elective and emergency Caesarean sections, postpartum haemorrhage, preterm birth and

stillbirth, in mothers aged over 40 was stronger that mothers aged 35-40. Therefore, the category

of women aged over 40 had greater risks of developing adverse pregnancy outcomes than the

category of 35-40. This cumulative adverse effect of maternal age on pregnancy outcomes

emphasizes more on the causal relation of AMA and adverse pregnancy outcomes.

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Studies reported a number of biological plausible mechanisms for the association of AMA and

pregnancy outcomes. It has been reported that the risk of adverse neonatal outcomes increases in

AMA women because of increasing in the risk of congenital malformations and chromosomal

anomalies, but after controlling for the confounding effect of congenital malformations, the

magnitude of associations was still exists (Luke & Brown 2007). In fact, maternal age can affect

the uterine blow flow and with advancing age, uterine blood flow decreases. Large uterine infarcts

and uteroplacental under perfusion were observed in AMA women (Pirhonen et al. 2005).

Additionally, a blinded invitro study showed that spontaneous myometrial contractility was

independently associated with maternal age, AMA may affect gap junction or ion channel

expression as well as control of electrical activity in the myometrium. Thus, impairment of

myometrial contractility may be a possible reason for higher risks of Caesarean section, prolonged

labour and emergency Caesarean section in AMA women (Smith et al. 2008). Furthermore, the

number of oxytocin receptor decreases with advancing age; consequently a higher dose of oxytocin

is needed to prevent postpartum haemorrhage in AMA women (Adasen et al. 1993). Based on

these studies, less efficient uterus in AMA women is expected and these theories emphasized more

on the fact that the association of AMA and adverse pregnancy outcomes is plausible.

3.1.6 AMA and placenta previa

As reported in studies, AMA is associated with a wide range of adverse pregnancy outcomes in

this regard uteroplacental insufficiency and placental dysfunctions were the basis of many adverse

pregnancy outcomes in AMA women (Miller 2005). One study reported that AMA was associated

with heavier placenta weight and that can be an explanatory reason for many adverse pregnancy

outcomes (Haavaldsen et al. 2011). Therefore, according to these studies, there was a clear effect

of aging on placenta functions. However, the result in previous studies about the effect of AMA

on placenta previa was inconsistent and studies with small sample size could not prove this

association (Wolff et al.1997, Favilli et al. 2012).

In several studies, it was unclear whether advanced maternal age was an independent risk factor

for placenta previa or was just demonstrative of more opportunity for adverse pregnancy outcomes

(Joseph et al. 2005, Carolan et al. 2013). In the study of Biro and associates (2012), AMA was

independently associated with placenta previa with the AOR of 2.2. However, the effect of ART

was not considered as a potential confounding factor.

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Based on the current evidence, the association of AMA with placenta previa was reported

differently. In some studies, the strength of association was strong, weak or in other studies no

associations were found. Thus, the findings of the current study will reinforce the association of

placenta previa with advancing maternal age.

3.2 Placenta previa

3.2.1 General aspects

Placenta previa is an obstetric complication in which the internal cervical os is partially or

completely covered by placenta, characterized by painless vaginal bleeding (Oyelese & Smulian

2006). Based on the position of placenta, four degrees of placenta previa have been described,

total, partial and marginal placenta previa as well as low-lying placenta. Although the internal

cervical os is not covered by low-lying placenta, a low-lying placenta may change to a partial

placenta previa in cervical dilatation (Cunningham et al. 2010).

The frequency of placenta previa decreases with gestational age and only 19% of patients, who

were diagnosed with placenta previa at second trimester, have the condition at 26-30 weeks

(Taipale et al. 1998). King (1973) used the term 'placenta migration' for the situation in which the

position of placenta is being renewed with advancing gestational age. Whereas, according to

trophotropism hypothesis, placenta develops in the direction of better vascularized positions

(Benirschke & Kaufmann 1990). Another explained theory is that apparent placenta migration is

due to growth of lower segment (Oyelese & Smulian 2006). The possibility of migration of anterior

placenta previa and low-lying placenta is more than posterior placenta previa (Cho et al. 2008).

3.2.2 Etiology and risk factors of placenta previa

The incidence of placenta previa was reported to be 0.3–0.5% and the magnitude of the excess risk

for placenta previa varied in different settings due to ethnic differences among populations (Iyasu

et al. 1993, Cresswell et al. 2013). The prevalence of placenta previa was common in Asian studies

(12.2 per 1000) and rare in Sub-Saharan Africa (2.7 per 1000). This wide disparity in the

prevalence of placenta previa can be explained by different trends in risk factors (Ahmed et al.

2016). For example, the incidence of placenta previa was high in populations with higher rates of

Caesarean section (Silver et al. 2006).

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The exact cause of placenta previa is not completely understood. However, several risk factors are

associated with placenta previa such as parity and multiple pregnancies. Ananth and associates

(1996a) reported that after adjusting for maternal age, the risk of placenta previa was higher in

women at parity 3 or higher than nulliparous women. In addition to that, the frequency of placenta

previa was 40% among multiple pregnancies in comparison to singleton pregnancies (Ananth et

al. 2003a). The possible explanatory reason for the association of multiple pregnancy with placenta

previa is that in the US along with increasing in the proportion of AMA women, the use of ART

has substantially increased so that twin pregnancies are more expected. Since placenta is larger in

twin pregnancies than singleton pregnancies, the possibility of having placenta previa is greater in

multiple pregnancies (Strong & Brar 1989).

The rate of Caesarean section in Finland is not high and it has been reported to be 16%, but studies

reported that Caesarean section both contributes and associates with placenta previa (EURO-

PERISTAT 2013, Räisänen et al. 2014). On the one hand, previous caesarean section is an

independent risk factor for placenta previa. On the other hand, the likelihood of giving birth by

Caesarean section was higher in nulliparous women with placenta previa. In a meta-analysis with

37 included studies was reported that after Caesarean delivery at first birth, the risk of placenta

previa increased by almost 2-fold in the following pregnancy (Gurol-Urganci et al. 2011). In

another study, not only the association between Caesarean section and subsequent placenta previa

was assessed, but also the relationship between additional Caesarean section and placenta previa

was evaluated. The results indicated that after three or more Caesarean sections the risk of placenta

previa increased more than 3-fold (Gilliam et al. 2002). Moreover, the impact of elective abortion

on subsequent pregnancy has widely discussed. One of the adverse effects of elective abortion,

especially multiple sharp curettage abortion method is a higher risk of placenta previa in

subsequent pregnancies. Furthermore, multiple prior abortions and abortions complicated by

infection are associated with an increased risk of placenta previa (Johnson et al. 2003).

From another point of view, the majority of women who underwent ART are AMA women and

after controlling for the effect of age, ART was an independent risk factor for placenta previa

(Johnston et al. 2015). The risk of placenta previa was 6-fold higher in pregnancies followed

assisted reproduction than naturally conceived pregnancies (Romundstad et al. 2006a). A

population based cohort study, comprising data of ART deliveries in Finland, Norway, Sweden

and Denmark from 1982 to 2007 reported that placenta previa was strongly associated with ART

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and the risk of placenta previa in ART pregnancies increased with advancing age till the age of 30,

while the risk of placenta previa in AMA women did not differ between ART and naturally

conceived pregnancies (Wennberg et al. 2016). A plausible reason for the association of ART with

placenta previa is that mechanical procedure of transferring the embryo in ART may cause

secretion of prostaglandin and more contractions. Thus, implantation in the lower uterine segment

was more common in pregnancies conceived by ART (Romundstad et al. 2006).

Uterine scarring from previous Caesarean sections, previous curettages and in general previous

surgeries on uterus contribute to the abnormal implementation of placenta. These situations lead

to suboptimal endometrial tissue, thinner myometrium, poor blood perfusion and less suitable

place of anterior uterine for placenta implantation. Therefore, placenta is implemented in the

healthier segments of the lower uterine (Gibbs & Danforth 2008).

Some studies reported that smoking is an independent risk factor for placenta previa (Ananth et al.

1996b, Salihu et al. 2003), while in other studies there was no association between smoking and

placenta previa (Rosenberg et al. 2011, Räisänen et al. 2014). One possible explanation, for the

positive association is that Carbone monoxide hypoxemia due to smoking contribute to

compensatory placenta hypertrophy. Thus, there is more possibility that placenta covers the

internal os of cervix. In smoking women with placenta previa, fibrinoid changes in maternal

decidual arteries, fibrotic peripheral villi, villous hyperplasia, and marginal placental necrosis were

reported for microscopic and macroscopic abnormalities of placenta (Williams et al. 1991).

Studies postulated that the risk of placenta previa has increased in recent decades. The rising rates

of placenta previa emanate mainly from changing risk profiles in women such as increasing in the

rates of Caesarean section and the mean of maternal age (Ahmed et al. 2016). Additionally, the

rise in the incidence of placenta previa was parallel with the growing use of ARTs. Study of

Nørgaard and associates (2012) reported that the introduction of In-Vitro Fertilization (IVF)

treatment since 1980, as a strong risk factor for placenta previa, was one of the contributing factors

for increasing placenta previa rate in Denmark.

3.2.3 Effect of placenta previa on maternal outcomes

The rising rates of placenta previa may add to the risks of various potential maternal complications

related to placenta previa. Women with placenta previa are exposed to more pregnancy

complications such as placental abruption, placenta accreta, postpartum anaemia, fetomaternal

17

haemorrhage, peripartum hysterectomy due to uncontrolled bleeding, postpartum haemorrhage,

second trimester bleeding, mal presentation and delayed maternal and infant discharge from the

hospital (Crane et al. 2000, Sheiner et al. 2001, Rubod et al. 2007, Rosenberg et al. 2011).

In the modern practice, in spite of all improvements in bleeding management during pregnancy

and availability of blood transfusion, haemorrhage is still a leading cause of maternal mortality

(Berg et al. 2010). More than half of pregnancies complicated by placenta previa experienced

antepartum haemorrhage and women with placenta previa and prior uterine scar may experience

higher risks of bleeding (Fan et al. 2017). In antepartum, placenta detachment sooner than deliver

or placental abruption may happen in women with placenta previa and cause massive bleeding that

can jeopardize the infant's life or contribute to maternal coagulopathy (Tikkanen et al. 2006, Gibbs

& Danforth 2008). In intrapartum, severe bleeding in pregnancies complicated by placenta previa

may result from abnormal placental attachments and contribute to emergency Caesarean section

and peripartum hysterectomy (Wong 2011). Suboptimal placenta place in the lower segment is

responsible for this firm attachment of placenta (Moretti et al. 2014). In addition to that, the results

of 50 placental bed biopsies of placenta previa cases showed trophoblastic cell invasion of

myometrial spiral arterioles was higher in placenta previa cases, which is the characteristic of

placenta previa with some degrees of placenta accreta (Biswas et al. 1999). In postpartum, the

lower segment of the uterus has less ability to contract than urine body so that abnormal bleeding

is expected after birth (Albayrak et al. 2011). From another point of view, higher frequency of

abnormal presentation, such as breech presentation and transverse lie, was reported in pregnancies

complicated by placenta previa (Seffah 1999, Bin et al. 2016). Placenta previa combined with

transverse lie are associated with maternal morbidities such as abnormal postpartum bleeding

(Kramer et al. 2011). Therefore, repeated bleeding in women with placenta during antepartum as

well as massive bleeding in intrapartum and postpartum may increase the risk of anaemia and in

women with anaemia, more adverse effects are expected in the postpartum period such as

depression (Bergmann et al. 2010) .

Pregnancies complicated by placenta previa have higher risks of antepartum, intrapartum and

postpartum hemorrhage. Bleeding and recurrent hospitalization impose higher emotional stress on

women (Katz 2001). Additionally, in women with diagnosed placenta previa costs related to

hospitalization, repeated ultrasound, higher risks of emergency Caesarean and longer stay in

18

hospital due to Caesarean section associated with a financial burden on patients and healthcare

systems (Eichelberger et al. 2011).

3.2.4 Effete of placenta previe on neonatal outcomes.

Suboptimal place of placenta in the lower uterine not only increases the risk of maternal

complications, but also increases neonatal adverse outcomes, including preterm birth, neonatal

death, perinatal death, respiratory distress syndrome, anaemia, congenital malformation. (Crane et

al. 1999, Lal & Hibbard 2015, Vahanian et al. 2015).

Preterm delivery is the major concern in women with placenta previa and the main focus in

placenta previa management is to reduce preterm birth and related consequences. Study of Erez

and associates (2012) showed that women with placenta previa who delivered before 37 weeks of

gestation or especially those who delivered before 34 weeks of gestation, had higher risks of

delivering preterm in subsequent pregnancy regardless of the position of the placenta. Thus, by

delivering infants as close as possible to the term, prematurity and related comorbidities can be

reduced (Oyelese & Smulian 2006). In a population-based study was reported that pregnancies

complicated by placenta previa experienced a 3-fold increase in the risk of neonatal mortality than

pregnancies without placenta previa and this association was mediated by preterm birth rather than

Small for Gestational Age (SGA) (Salihu et al. 2003). However, according to the results of

Nørgaard and colleagues (2012), even in term pregnancy high risk of low Apgar score of ≤7 at 5

minutes and NICU admission was expected in pregnancy with placenta previa. Additionally, in

the study Ananth and associates (2003b) infants delivered at 36 weeks of gestation had lower rates

of neonatal death than pregnancies without placenta previa. This can partly be explained by the

reason that women with placenta previa received more steroid for infant lung maturity.

In women with placenta previa infants have 210 g lower mean birth weight than pregnancies

without placenta previa and prematurity mediate the association of placenta previa with low birth

weight, but not with SGA (Ananth et al. 2001, Walfisch and Sheiner 2016). Additionally, Räisänen

and associates (2014) reported that after controlling for possible confounding factors in

multiparous women, placenta previa was a risk factor for SGA, but SGA was not associated with

placenta previa in nulliparous women. Although the association of placenta previa with SGA is

due to placenta insufficiency, Happer and associates (2010) reported that placenta previa was not

always identified with uteroplacental insufficiently and in their study, placenta previa was not

19

associated with birth weight less than 15th percentile and less than 10th percentile. Moreover, high

risks of preterm birth, low birth weight, SGA and low Apgar score in pregnancies with placenta

previa, increased the risk of an infant being admitted in NICU (Vahanian et al. 2015).

3.2.5 Effect of risk factors on placenta previa outcomes

Most research has focused on adverse maternal and neonatal outcomes of placenta previa and the

impact of other risk factors on the outcome of placenta previa has rarely assessed. Clinical

outcomes of placenta previa vary from woman to another woman and they cannot be entirely

anticipated. However, some risk factors may worsen the adverse outcomes of placenta previa. For

example, study of Silver and associates (2006) showed that prior Caesarean sections coupled with

placenta previa added to the risk of placenta accreta. Additionally the likelihood that placenta

previa remains previa till term is higher in women with prior Caesarean sections due to previous

damage to the uterine (Dashe et al. 2002). Moreover, women with placenta previa and prior

Caesarean sections had higher risks of massive bleeding during Caesarean section (Hasegawa et

al. 2009). These examples illustrate that a risk factor such as prior Caesarean section can magnify

the adverse outcomes of placenta previa.

Nowadays, women are postponing pregnancy for many reasons and it is likely that AMA is

associated with adverse pregnancy outcomes, because of uteroplacental insufficiency and the

effect of aging on the vessels. It is conceivable that the combination of AMA with suboptimal

place of placenta in placenta previa may worsen maternal and neonatal outcomes. However, the

extent to which AMA is associated with adverse outcomes of placenta previa has not been

researched. This thesis attempts to evaluate the potential negative effects of AMA on maternal and

neonatal outcomes of placenta previa.

3.3 Register-based study

3.3.1 Register-based data in pregnancy research

There are practical questions and concerns for couples regarding adverse pregnancy outcomes and

sometimes providing answers and giving assurance require conducting epidemiological research.

In Finland, the first perinatal studies were based on three large birth cohorts, but usually cohort

studies are expensive and time-consuming (Gissler et al. 1997). Nowadays, many countries have

20

introduced birth registries as a valuable source of epidemiological studies of this kind, with

coverage of 97% to 100% of the population (EURO-PERISTAT 2004).

Existing register-based data generate important hypotheses to start research. Retrospective

evaluation of birth registers has been used to evaluate the effect of social characteristic on birth

outcomes and the effect of prenatal period on pregnancy outcomes. Furthermore, many

complications have a long latency period and availability of data enables researchers to evaluate

the association between stressors in pregnancies and increased risk of diseases in offspring (Gissler

et al. 1997). Birth registries are also used as a surveillance tool to evaluate the effects of early life

exposures on birth defects. Moreover, the linkage between registers made it possible for

researchers to evaluate the effect of different pregnancy complications on the risk of developing

later disease such as the link between preeclampsia and developing cardiovascular disease in the

future (Wilcox 2007). Birth registries are used to monitor the health of women and their newborns

and have provided a valuable infrastructure for evidence base medicine and clinical practice (Olsen

2011). For example, evidence based medicine considerably changed the obstetric practice of

breech delivery in Sweden (Alexandersson et al. 2005).

Central Population Register, Cause of Death Register and Medical Birth Register (MBR) cover all

live births and stillbirths in Nordic countries, but more detailed information is collected in MBR

(Gissler 2010). Along with thalidomide catastrophe, the first national MBR was set up in 1967 in

Norway with the purpose of epidemiological surveillance of birth defect (Irgens 2000). Finland

established the MBR in 1987 latest than the other Nordic countries. MBR has been used alone or

combined with other national birth registries and data from interviews or questionnaires (Gissler

et al. 1997). In Finland, perinatal statistics are published every second year by the National Institute

for Health and Welfare (THL) which is responsible for birth registers (Gissler 2010).

3.3.2 Strengths and limitations of register-based research

The number of register-based studies have substantially grown. Register-based data have made it

easier to develop epidemiological research, especially in the field of rare complications. Detection

of rare complications requires large cohort of a hundred thousand to provide enough statistical

power. A large number of participants in registries increase statistical power and uncovers the

hidden patterns of correlations in rare complications (Olsen 2011). According to Rothman and

associates, (2008) large datasets increase the precision of estimation and reduce random error.

21

Along all advantages of having a large sample size in observational studies, large sample sizes

may provide significant results to clinically unimportant associations. Therefore, in analyzing

register-based data with a large sample size not only, the statistical significance is important, but

also the strength of the association needs to be taken into account (Thygesen & Ersbøll 2014).

Additionally, thanks to the unique system of identification numbers, a number of studies has been

designed to link data sources to enhance epidemiological and clinical research. These linked

datasets sometimes are known as “big data”, but the complexity of big data requires technical and

human resources to operationalize data in research practice (Meyer et al. 2014).

One of the objectives of epidemiological studies is to find valid and precise associations between

exposures and outcomes. Comprehensive data enable the researcher to understand the underlying

mechanisms of different complications and provide opportunities for prevention and treatment. In

today's practice, use of register-based data in the Nordic countries and some other countries has

made research easier, less time consuming and cost-effective (Irgens 2001). Register-based

research represents associations in the real world and the value of register-based data increases

over the time periods, because more time and people will be added to the data. For example, in the

birth registry as more and more birth accumulated, gave researcher the possibility of evaluating

different risk factors on pregnancy outcomes (Wilcox 2007).

Comprehensive register-based data provide an opportunity to consider all confounding factors and

give insights into real causal associations. However, this optimal situation is not always the case,

because it is not possible to adjust for confounding factors that they are not already exist in the

data (Olsen 2011). In register-based study, variable collection is the responsibility of the

administrators and the researcher has no controls over the content of variables. Since register-

based data are not originally gathered for the research in selected hypothesis, collection of the data

may be less precise than the real situation. Additionally, the information of some confounding

variables is available in a limited period of time so that incomplete adjustments for potential

confounding factors may distort the direction of the associations and affect the quality of the study

(Weiss 2011). For example, in Finland, MBR contains information on a limited number of

maternal characteristics and birth outcomes and lack of information about maternal weights before

2003 delayed development of appropriate evaluation of maternal weight on pregnancy outcomes

(Gissler et al. 1997, Lamminpää 2015). Thus, selection of included variables needs more careful

attention. In this regard, the MBR content has been changed three times to improve the quality of

22

the data and some variables such as maternal background information and outcomes were added

into the MBR in 2004 (Räisänen 2011).

Another limitation of register-based data is missing data. Underlying reason for missing data is

usually hard to find, but depending on the extent, missing data increases the risk of bias. In

handling missing data, having a specific strategy to eliminate the impact of missing values can

enhance the interpretation of the result (Gliklich et al. 2014). Moreover, in register-based study,

exposure or outcome may be misclassified and this misclassification will happen with the same

probability for all study individuals. Consequently, non-differential misclassification of

dichotomous variables tends to change the direction of association toward the null and if the

exposure is not dichotomous, results may be away from the null (Rothman 2002). From another

point of view, since the register-based data have been retrospectively obtained, the information

about the exposure is less influenced by later diagnosis of the outcome of interest. Thus, there is

less potential for recall bias. For example, register-based studies on the use of medicine in

pregnancy, oral contraceptives, is less likely to introduce recall bias (Nørgaard et al. 2009).

Before using the register-based data, reliability and validity of register-based data should be

assessed. Validity in the sense that all recorded variables are in the line with the reality. However,

in register-based data, sometimes there are variations in the quality of some variables which restrict

the usage of variables in statistics and research (Gissler & Haukka 2004). In MBR, the validity of

data depends on reporting and they are considered to be valid, if variables have been correctly

reported (Räisänen 2011). Additionally, the validity of the registers can be determined by its

completeness related to the coverage of all individuals in the registers (Thygesen & Ersbøll 2014).

Poor quality of registries may contribute to the suspension of registry such as suspension of the

Finnish Register on the Mentally Retarded (Gissler & Haukka 2004).

In public health research, the potential harms of interventions usually stem from physical adverse

effects of the intervention, while in register based research, the potential harms of research emanate

from overstepping the individuals' privacy. Data in register-based research is anonymous, but the

identification of people is not impossible in combination data. Providing high quality data

protection ensures confidentiality and minimizes discrimination, stigmatization and social

problems in individuals. However, having informed consent from thousands of individuals is not

applicable to register-based research and it is time consuming, expensive and sometimes

23

impossible. Therefore, public benefits of the register-based research should compensate the

potential harms to individuals’ autonomy (Eloranta et al. 2015).

The combination of all birth registers can provide a good source of collaborative projects especially

in rare outcomes. However, there are some variations in the content of registers. Uniform

documentation of national registers, such as standardizing the definition of stillbirths or developing

cause of infant death, will improve the quality of register-based studies at the international level

(Gissler et al. 2010, EURO-PERISTAT 2013). Mutual collaboration between centers and register

controllers, coupled with constant use of register-based research as a source of evidence based

medicine and decision making will improve the quality of the data (Gissler 2008).

24

4 AIMS OF THE STUDY

The aim of this registered-based cohort study was to assess the association of AMA with placenta

previa, after adjusting for possible maternal and obstetric confounding factors. The second aim of

the study was to evaluate the effect of AMA on placenta previa outcomes, including blood

transfusion, placental abruption, preterm birth, NICU admission, low birth weight and low Apgar

score at 5 minutes, using data of three Finnish health registries from 2004 to 2008, including

information of 283 324 women and their newborns. Therefore, the objectives were:

1. To evaluate the association of maternal age ≥35 years old with placenta previa.

2. To compare maternal and neonatal outcomes in AMA women with placenta previa to younger

women with placenta previa.

25

5 METHODS

5.1 Study population

The study was a register-based cohort study, using the data of three Finnish health registries with

the information of 283 324 women and their newborns, from 2004 to 2008. The data contained the

information about MBR, The Hospital Discharge Register (HDR) and The Register of Congenital

Malformations. The information of The Register of Congenital Malformation was used as an

exclusion criterion to exclude pregnancies with major congenital anomalies. In addition, women

with multiple pregnancies were excluded, because they impose higher risks on pregnancy

outcomes. Cross-linkages of registers were done by the use of personal identity codes to increase

the completeness of the data.

5.1.1 Medical Birth Register (MBR)

Finland started the Medical Birth Registry in 1987. The MBR covers more than 99.9 % of births

in Finland and contains detailed information about previous pregnancies and deliveries, maternal

care, diagnosis and interventions as well as newborn health status, diagnosis and interventions up

to 7 days. The purpose of MBR is to collect data to improve maternal and neonatal care services

in Finland (THL 2015a).

5.1.2 Hospital Discharge Register (HDR)

The HDR was established in 1969. HDR contains information such as hospitalization period,

procedure, and diagnosis, on all aspects of inpatient care in public and private hospitals as well as

outpatient visits in public hospitals. Diagnoses are coded by using the International Classification

of Diseases (ICD) codes (THL 2015b).

5.1.3 Register of Congenital Malformation (RCM)

The Register of Congenital Malformations was established in 1963 and separately liked to MBR.

The RCM includes the data on congenital, chromosomal and structural anomalies detected in

stillborn and live born infants from all Finnish healthcare settings. The RCM also includes

information of abortions due to fetal defects. The register has the follow up period of 6-12 months

(THL 2015c).

26

5.2 Variables and definitions

Variables in the MBR data are usually recorded by nurses or midwives during obstetric visits in

pregnancy. In this study, gestational age was estimated by ultrasonography during first and second

trimesters or calculated according to last menstrual period. Antenatal visits were categorized as

less than 10 visits, 10 to 15 visits and more than 16 visits, since the number of 10 to 15 visits is

recommended by Finnish national maternity guidelines. Smoking was self-reported variable and

categorized in three categories including non-smokers, smoking only during the first trimester and

smoking throughout pregnancy. There was no information regarding the number of cigarettes

smoked per day.

5.2.1 Independent variables

Based on the most common definition of AMA, maternal age was categorized into two age groups

less than 35 years and 35 years or older, with a reasonable size of the population in both categories.

Women less than 35 years were selected as the reference group and this age group contained the

median age of women in this population.

For the second aim of this study AMA and placenta previa were considered together as exposure

variables. In order to provide better visual evaluation, maternal age was categorized as 20-24 years,

25-29 years, 30-34 years, 35-39 years, 40-44 years and 45-50 years, to assess the unadjusted risk

of maternal age on placenta previa outcomes. In addition to AMA, placenta previa was considered

as an independent variable. Placenta previa was a dichotomous variable without any further

explanation about the type of placenta previa, defined as low-lying placenta covering internal os

of cervix. In ICD-10 coding, placenta previa is represented by the O44 diagnosis code. In our

population, placenta previa was diagnosed by ultrasound in the second and third trimester and the

time of accruing placenta previa was considered as occurring at diagnosis time.

5.2.2 Dependent variables

In assessing the effect of maternal age on placenta previa outcomes, we sets two sets of outcomes,

maternal and neonatal outcomes. The main maternal and neonatal outcomes were blood

transfusion, placental abruption, preterm birth, NICU admission, low birth weight and low Apgar

score at 5 minutes . Placental abruption was diagnosed by clinical examinations and in some

27

women by ultrasonography. Variable blood transfusion was coded as (yes) for women who

received any transfusion of blood products. Infants less than 2500 g were classified as low birth

weight. Preterm birth was recorded for births less than 37 weeks of gestation. Apgar scores

between 0-6 were recorded as low Apgar scores at 5 minutes. Apgar score is defined by a scoring

method and used by birth attendant; a score less than 7 is usually considered a low Apgar score

(Apgar 1953). NICU admission was considered when infants were intubated. The second aim of

this thesis poses if the risk of adverse maternal and neonatal outcomes of placenta previa increase

more in AMA women than younger women.

5.2.3 Confounding variables

The effect of maternal age on placenta previa can be easily distorted by the effect of confounding

factors. This distorted effect of confounding factors can overestimate or underestimate the effect

of maternal age on placenta previa. In this thesis, in order to deal with the effect of confounding

variables, variables were interred in the regression modelling.

For the association of AMA and placenta previa parity, smoking, prior Caesarean section, use of

ART such as IVF and embryo transfer , Intra-Uterine Insemination (IUI), amniocentesis, were

considered as potential confounding variables. In evaluating the effect of age on adverse placenta

previa outcomes, parity, smoking, previous Caesarean section as well as obstetric interventions

such as use of ART were considered as potential confounding factors.

In this study, preterm birth was not considered as a confounding variable for the association of

placenta previa and adverse neonatal outcomes, because preterm birth was in the pathway of the

association. Although preterm birth is associated with both placenta previa and adverse neonatal

outcomes, preterm birth is a consequence of placenta previa. Therefore, the effect of placenta

previa and maternal age on low birth weight, low Apgar score at 5 minutes and NICU admission,

considered without adjustment for the gestational duration.

5.3 Design of the study

A registered-based cohort study was performed to assess tow research questions. For the first aim

of the study, maternal age 35 years or older was considered as exposure and maternal age less than

35 as a reference group. The outcome of interest was placenta previa which is a rare pregnancy

28

outcome. Thus, a register-based data of three birth registers provided better power to find the

association.

For the second aim of the study, In order to assess the unadjusted risk, figures were plotted to

evaluate the proportion of adverse pregnancy outcomes with advancing age in women with and

without placenta previa. Therefore, figures gave the unadjusted risk of adverse pregnancy

outcomes in women with and without placenta previa. In this regard, if the distance of two curves

got larger at an advanced age, the hypothesis was correct.

In order to analyze the adjusted risk of adverse pregnancy outcomes in AMA women, placenta

previa at AMA women was considered as the main exposure to be studied. We formed four

comparison groups, younger women with no previa, younger women with previa, AMA women

without placenta previa and AMA with placenta previa, to evaluate what placenta previa and age

bring up at the same time.

5.4 Statistical analysis

Statistical analysis was conducted using SPSS version 22. Demographic and clinical

characteristics of the study population were compared between women less than 35 years and 35

years or older by using Chi-Square test. The association between advanced maternal age and

placenta previa was modelled using a backwards, stepwise logistic regression to calculate adjusted

odds ratios and 95% confidence intervals by considering placenta previa as a dichotomous

variable. Confounding factors were selected based on literature review or significant associations

in bivariate analysis and variables without statistically significant results in bivariate models were

checked in logistic regression to ensure the same results. Multivariable logistic regression

modelling was used to investigate the association of placenta previa and maternal age with adverse

maternal and neonatal outcomes by considering the effect of confounding variables. Results were

reported in adjusted odds ratios with 95% CI and P value, with a P value < 0.05 considered

significant. The goodness of the final logistic model was examined using the Hosmer–Lemeshow

goodness of fit test.

5.5 Ethical considerations

The Ethical approval for this study was gained on 17.05.2016 (THL/151/5.05.00/2016) from

THL, which is required by the national data protection legislation.

29

6 RESULTS

6.1 Characteristics of the studied population

A total of 283 324 women, of which 714 (0.3%) were complicated by placenta previa. 81.2% of

women were less than 35 years of age and 18.8% of women were 35 years or older. The median

age at delivery was 29, range from 14 to 52 years.

In our population, younger women were more likely single and smokers and older women were

more likely multipara. AMA women had higher numbers of antenatal visits, amniocentesis and

use of ART like embryo transfer were more common in AMA women. Older women had a higher

proportion of pathological glucose tolerance test and were hospitalized more due to bleeding and

high blood pressure. Vaginal delivery was performed more in women less than 35 years who were

also more likely to have forceps delivery. However, a greater proportion of AMA women had

planned and unplanned Caesarean section. There were no statistically significant differences in the

proportion of women with anaemia between younger and older group. The demographic

characteristics of women are summarized in Table 1.

30

Table 1. Demographic and clinical characteristics for women giving birth in Finland from 2004-

2008, stratified by maternal age.

Characteristics <35y

(n=230003)

≥35y

(n=53321)

P value1

Marital status

0.000

Single (unmarried, widowed or divorced) 96448(41.9%)2 17682(33.2%)

Married or in a registered partnership 133357(58.0%) 35591(66.7%)

Parity

0.000

Primipara 107523(46.8%) 12087(22.7%)

Multipara 122082(53.2%) 41141(77.3%)

Smoking status

0.000

Non-smoking 187420(81.5%) 46561(87.3%)

Quitted smoking after first trimester 9468(4.1%) 1077(2.00 %)

Smoking 26995(11.7%) 4141(7.8%)

Embryo transfer 3550(1.5%) 2161(4.1%) 0.000

Amniocentesis before 25 weeks of gestation 2534(1.1%) 5024(9.4%) <0.001

Pathological glucose tolerance test 18744(8.10 %) 7438(14.0%) <0.001

Hospitalization

<0.001

Due to high blood pressure 6612(2.9%) 2114(4.0%)

Due to a threat of prematurity 6795(3.0%) 1427(2.7%)

Due to bleeding 2452(1.1%) 716(1.3%)

Due to other reasons 25255(11.0%) 6842(12.8%)

Mode of delivery

<0.001

Vaginal delivery 175680(76.4%) 37643(70.6%)

Forceps delivery and suction cup 17864(7.8%) 3513(6.6%)

Planned Caesarean section 14353(6.2%) 5936(11.1%)

Unplanned Caesarean section 21772(9.5%) 6152(11.5%)

Number of antenatal visits

Number of antenatal visits more than 15 123659(56.3%) 29402(58.1%) <0.001

1 Chi-Square, 2 Values are number and percentage.

31

6.2 Effect of AMA on placenta previa

Table 2 depicts the association between AMA and other risk factors with placenta previa. The

results of regression model showed that AMA was significantly associated with placenta previa

AOR 1.54 (1.30-1.83), with a 54% increased risk of placenta previa. Moreover, amniocentesis,

IVF, embryo transfer and previous Caesarean section were identified as independent risk factors

for placenta previa.

Table 2. The association between advanced maternal age and placenta previa and other risk

factors.

Variables 1 AOR2 and 95% CI P-value 3

Maternal age ≥35y 1.54 (1.30–1.83) <0.001

IVF4 7.93 (2.91–21.58) <0.001

Embryo Transfer 5.61 (4.41–7.14) <0.001

Other ART5 3.15 (1.16–8.53) 0.023

Amniocentesis 1.68 (1.21–2.33) 0.002

Previous Caesarean section 1.57 (1.26–1.97) <0.001

1 Variables included in the model one were maternal age, parity, smoking, previous Caesarean

section, IVF, IUI, embryo transfer, other ART and amniocentesis.2 Adjusted Odd Ratio, 3 Final

model of backwards, stepwise logistic regression, 4 In Vitro Fertilization, 5 Assisted Reproductive

Technology.

6.3 Unadjusted effect of maternal age on placenta previa outcomes

Figure 1 and 2 depict unadjusted risks between maternal age and adverse maternal and neonatal

outcomes in women with and without placenta previa. Lower curves illustrate the age-specific

risks of maternal and neonatal outcomes in women without placenta previa, while upper curves

show age-specific risks of adverse maternal and neonatal outcomes in pregnancies complicated by

placenta previa.

32

Figure 1. Relationship between maternal age and maternal outcomes, by placenta previa (upper

curves denote women with placenta previa and lower curves women without placenta previa), from

2004 to 2008 in Finland. (A) Blood transfusion, (B) placental abruption, with 95% CI error bars.

Graphs are plotted on the scale of 0-100%

As Figure1 depicts, in pregnancies complicated by placenta previa, risks of blood transfusion and

placental abruption increased in younger women and decreased in intermediate aged mothers. The

effect of AMA compound with placenta previa, leading to increasing risks of blood transfusion

and placental abruption in women with placenta previa. The more women delayed the pregnancy,

the more they had maternal risks.

As Figure 2 illustrates, risks of neonatal outcomes gradually increased with advancing age in the

background population. However, the proportion of adverse neonatal outcomes in pregnancies

complicated by placenta previa was not consistent across maternal age groups. It was observed

that women aged 40-44 years had the highest risks of preterm birth, NICU admission and low birth

weight.

33

Figure 2. Relationship between maternal age and neonatal outcomes, by placenta previa (upper

curves denote women with placenta previa and lower curves women without placenta previa), from

2004 to 2008 in Finland. (A) Preterm birth, (B) NICU admission, (C) low birth weight, (D) low

Apgar score at 5 minutes, with 95% CI error bars. Graphs are plotted on the scale of 0-100%.

34

6.4 Adjusted effect of maternal age on placenta previa outcomes

The adjusted odds ratios for adverse maternal and neonatal outcomes, with considering women

without placenta previa as a reference group, are given in Table 2,3. Pregnancies complicated by

placenta previa had higher risks of blood transfusion, placental abruption, preterm birth, NICU

admission, low birth weight and low Apgar score at 5 minutes, than women without placenta

previa. However, the odds for blood transfusion and placental abruption were slightly greater in

AMA women with placenta previa than younger women with placenta previa. Furthermore, the

magnitude of the associations, for adverse neonatal outcomes, was stronger in women under age

35 with placenta previa, compared with older women counterparts (Table 2, 3).

Table 2. Pregnancy outcomes in women under age 35 with and without placenta previa.

Outcomes Without placenta

previa

229506(99.8%)1

With placenta

previa

496(0.2%)

AOR( 95% CI)2 P-value3

Blood transfusion 3710 (1.6%) 67 (13.5%) 6.86 (5.26-8.95) <0.001

Placental abruption 493(0.2%) 12(2.4%) 10.95(6.11-19.61) <0.001

Preterm birth 12532(5.5%) 208(42.0%) 11.76 (9.78-14.15) <0.001

NICU admission 25609(11.2%) 195(39.3%) 4.93 (4.10-5.94) <0.001

Low birth weight 9741(4.3%) 111(22.4%) 5.95(4.77-7.41) <0.001

Low Apgar score at 5

minutes

4507(2.0%) 28(5.7%) 3.39 (2.29-5.01) <0.001

1 Values are number and percentage. 2Adjusted Odds Ratio (AOR) and 95% Confidence Intervals

(95%CI). Adjusted for parity, smoking, previous Caesarean section, Assisted Reproductive

Technology (ART) and Caesarean section. 3 Statistical analysis was conducted with

multivariable logistic regression model.

35

Table 3. Pregnancy outcomes in women aged 35 or older with and without placenta previa.

Outcomes without

placenta previa

53103(99.6%)1

with placenta

previa

218(0.4%)

AOR( 95% CI)2 P-value3

Blood transfusion 987(1.9%) 37(17.0%) 7.31(5.04 -10.63) <0.001

Placental abruption 167(0.3%) 8(3.7%) 11.32(5.48-23.39) <0.001

Preterm birth 3496(6.6%) 91(41.7%) 8.84(6.69-11.69) <0.001

NICU admission 6844(12.9%) 89(40.8%) 4.06(3.08-5.37) <0.001

Low birth weight 2755(5.2%) 46(21.1%) 4.06 (2.89-5.71) <0.001

Low Apgar score at 5

minutes

1137(2.1%) 13(6.0%) 2.78(1.56-4.96) 0.001

1Values are number and percentage. 2Adjusted Odds Ratio (AOR) and 95% Confidence Intervals

(95%CI). Adjusted for parity, smoking, previous Caesarean section, ART and Caesarean section.

3 Multivariable logistic regression.

36

7 DISCUSSIONS

7.1 Interpretation of the results

In Finland, the proportion of maternal age 35 years or older was 18.8 %, during 2004-2008.

Reviewed articles on women age ≥35 years reported different values for the proportion of AMA,

ranging from 10.1% in china, to 19% in England and Wales and to 24% in Australia (Crane &

Morris 2006, Biro et al. 2012, Xiaoli & Weiyuan 2014).

The results of clinical and demographic characteristic of women were consistent with previous

studies and showed that AMA women were more likely to have medical conditions, including high

blood pressure and abnormal glucose tolerance which reflects age-related changes in AMA women

(Paulson et al. 2002). We found that AMA women had a higher proportion of planned and

unplanned Caesarean section. As reported in studies, planned Caesarian section without any

indications placed this age group of women in an unnecessary increased risk of adverse pregnancy

outcomes. However, induction of labour in women age 35 years or older had no significant adverse

effects on maternal and neonatal outcomes (Janoudi et al. 2015, Lavecchia et al. 2016, Walker et

al. 2016). Women less than 35 years old in our study were more single and smokers which indicate

younger women had more social problems than medical conditions.

Among the total number of women during 2004 to 2008, placenta previa complicated 0.3% of

deliveries, which is in the lower range of the reported rates of 0.3– 0.5% (Iyasu et al. 1993). We

found that AMA was an independent risk factor for placenta previa and several cofactors

confounded the association of AMA with placenta previa. In our population, parity and smoking

did not confound the association, while in some studies parity and smoking were recognized as

strong risk factors for placenta previa (Ananth et al. 2001, Walfisch & Sheiner 2016). In the logistic

regression model use of ART was the main risk factor for placenta previa which is consistent with

previous studies (Coroleu et al. 2002, Johnston et al. 2015). However, different techniques of ART

contributed differently to the risk of placenta previa, IVF and embryo transfer were the most

contributing risk factors in the final model. Our finding reinforced that the risk of placenta previa

increases with advancing maternal age (Biro et al 2012, Ludford et al. 2012, Liu & Zhang 2014).

From the epidemiological aspects, studies reported higher risk of placenta previa in women aged

older than 40 years than women aged 35-39, which means that there is a biological gradient for

the association of AMA and placenta previa (Jolly et al. 2000, Cleary-Goldman et al. 2005). One

of the explained biological mechanism for increasing the risk of placenta previa in AMA women

37

is decreasing in the uterine blood flow in older women so that larger place for placenta is needed

to provide enough blood flow to the fetus (Ananth et al. 1996).

AMA and placenta previa were accounted as independent risk factors for bleeding. Some studies

reported that pregnancies complicated by placenta previa were associated with massive

intrapartum, antepartum and postpartum bleeding and the risk was noticeable among women with

complete placenta previa (Sekiguchi et al. 2013, Bao et al. 2016). Other studies have identified

AMA and Very Advanced Maternal Age (maternal age ≥ 45 years) were independently associated

with increased need of blood transfusion (Phadungkiatwattana et al. 2014, Haslinger e al. 2016).

The result of these studies did not account for the possible effect of maternal age on the association

of placenta previa with blood transfusion. In the current study as illustrated in Figure 1A, apart

from high proportion of blood transfusion in early 20s, the proportion of blood transfusion had a

gradual increase in AMA women, reaching more than 20% risks of blood transfusion in women

aged 40-44 years with placenta previa. After controlling for the effect of confounding variables,

including previous Caesarean section, the result supports the concept that AMA women with

placenta previa had a higher AOR of blood transfusion than younger women with placenta Previa.

This can be partly explained by one in vitro study in which AMA was associated with a decrease

in spontaneous uterine contraction (Smith et al. 2008).

Previous reports regarding the risk of placental abruption in AMA women were different. One

study reported that the risk of having placental abruption was higher in younger women, while the

majority of the analyses found that there is a strong association between AMA and placental

abruption (Sheiner et al. 2003, Liu & Zhang 2014). In the current study, the proportion of placental

abruption was highest among women aged 20-24 and 40-44 years with placenta previa and this

disparity across age groups was not exist in women without placenta previa. However, after

adjusting for confounding factors including smoking, the odds ratio of having placental abruption

was stronger in AMA women with placenta previa than younger women with placenta previa.

The association of maternal age and preterm birth have been accessed frequently and results

indicated that the effect of maternal age on preterm delivery had the familiar U-shaped pattern

with high risks of preterm delivery in adolescents and older women (Ferré et al. 2016). Although

studies indicated higher risks of preterm birth in AMA women and pregnancies complicated by

placenta previa (Zlatnik et al. 2007), in our study, the risk of preterm birth in women with placenta

previa was not additive with advancing maternal age. In the study of Lawlor and associates (2011),

38

the lowest rate of preterm birth across age groups was reported among women aged 24-30 years.

Whereas, in the current study in women with placenta previa, mothers aged 20-24 years had the

lowest proportion of preterm birth.

Since preterm birth is more common in pregnancies complicated by placenta previa, it may affect

the risk of other adverse outcomes. In other words, preterm delivery is an intermediate or surrogate

outcome on the causal path from placenta previa to low birth weight, low Apgar score at 5 minutes

and to NICU admission. Thus, in this study in order to prevent overadjustment which increases

the net bias, preterm birth was considered as an intermediate outcome on the causal path from

placenta previa to neonatal outcomes (Schisterman et al. 2009).

In previous studies, the risks of LBW had a U-shaped pattern across age groups, with high risk of

LBW in younger and older mothers, at two ends of maternal age. In the unadjusted model, the

proportion of LBW infants in pregnancies complicated by placenta previa across age groups had

the same pattern with the highest proportion of LBW among mothers age of 40-44 years (Figure

2 C). In this study, the presence of placenta previa in pregnancy increased the risk of an infant

being admitted to NICU, which is consistent with the result of a recent systematic review

(Vahanian et al. 2015). In previous studies, the risks of NICU admission were not affected by

AMA, but one study reported that AMA women with preeclampsia had higher risks of NICU

admission than women less than 35 years with preeclampsia (Lamminpää et al. 2012). In our study

the odds of NICU admission in women less than 35 with placenta previa were higher than AMA

women with placenta previa, which means age-related outcomes may be differently affected in

various complications during pregnancy.

In neonatal outcomes after controlling for the effect of gestational age on low birth weight and low

Apgar score, the causal associations disappeared. Therefore, in pregnancies complicated by

placenta previa, low birth weight and low Apgar score are mediated by the real effect of gestational

age and there were no other significant causal associations. One explanatory reason for the higher

rate of preterm birth and other related complications in younger women with placenta previa is

that AMA women have lower oxytocin receptor than younger women so that the contractility of

the uterus decreases in older mother and the uterus is less susceptible to premature contracting

process of preterm birth (Lisonkova et al. 2011).

39

7.2 Strengths and weaknesses

The main strength of this study is the number of participants. A Large number of participants in

this study provided the possibility of considering important confounding factors and studying

sufficiently rare outcomes. Placenta previa is a rare pregnancy complication consequently a large

sample size is needed to detect small differences in risks between two groups of maternal age.

Additionally, the comprehensiveness of this data made it possible to consider the effect of different

techniques of ART on placenta previa. From another point of view, since placenta previa is a rare

pregnancy complication, there is a possibility of being under reported based on antenatal routines.

However, the data were drawn from well-established registries and there was only one missing

case for the variable placenta previa. According to Gissler and Haukka (2004), the register-based

data in Finland have adequate coverage and validity.

On the other hand, the data were not originally collected for this study hypothesis and information

on the severity and grade of placenta previa were not available. The information on low Apgar

score at 5 minutes was missing in 15.2% of participants. Therefore, the result of this variable needs

to be interpreted with caution. There is no information about the level of education for considering

socioeconomic characteristic of the participants as a confounding factor. As previously discussed,

AMA women are more educated and the level of education may have positive effects on the

outcomes.

7.3 Generalizability of the results

The results of this study are generalizable to other population in which healthcare services are

freely and similarly available to all women. Finland has ethnically homogenous population and

the results can be generalized to populations with similar structure. Furthermore, the effect of risk

factors on placenta previa may have substantial variations between countries, because of

differences in baseline characteristics of the studied populations. For example, disparity in the rate

of Caesarean section, from 16% in Finland to 38% in Italy, can affect the magnitude of the risks

among populations (EURO-PERISTAT 2013). Thus, the generalizability of the results requires

careful thought, because risk factors of placenta previa may contribute differently to the risk of

placenta previa across countries.

40

7.4 Implications for practice

The results of this study showed the effect of age on the risk of placenta previa and determined

how AMA can affect the observed risks of placenta previa outcomes. The results emphasized more

on the effect of AMA on maternal morbidity and adverse outcomes. Thus, growing trends toward

postponing pregnancy in many countries could be an emerging public health concern and need to

be taken more seriously. It is necessary to inform women about possible adverse effects of

postponing motherhood, while respecting women's reproductive autonomy (Lemoine & Ravitsky

2015).

AMA women usually want to have optimal birth outcomes, because they have postponed

childbearing to the older age or they underwent ART. However, the occurrence of comorbidities

in AMA women may negatively affect pregnancy outcomes. The results of this study indicated

that AMA women had a higher possibility of using ART or having preexisting medical conditions.

Additionally, AMA independently affected the risk of other complications such as placenta previa

and may worsen the risk of blood transfusion and placental abruption in pregnancies complicated

by placenta previa. These comorbidities may further influence the risk of adverse pregnancy

outcomes and pose major challenges for healthcare professionals. Therefore, the finding of this

study will provide additional guidance for clinicians to identify high-risk groups in order to

improve prenatal, antennal and neonatal care of older age women.

Based on the literature review, one of the main causes of haemorrhage in pregnancy is placenta

previa and pregnancies complicated by placenta previa had a higher risk of placenta accreta. Based

on the results of current thesis, higher risks of placental abruption and blood transfusion were

expected in pregnancies complicated by placenta previa especially in AMA women. Hence,

detailed examination of placental morphology by early ultrasound screening is necessary to rule

out placenta accereta (Moretti et al. 2014). Moreover, sufficient preparation for blood transfusion

is needed to prevent further maternal and neonatal adverse outcomes in pregnancies complicated

by placenta previa.

7.5 Implications for research

This study presented the effect of AMA on placenta previa as well as the effect of maternal age on

adverse pregnancy outcomes of placenta previa in Finnish women from 2004 to 2008. The results

showed that AMA is a risk factor for many pregnancy complications and it is recommended to

41

include maternal age in risk factor analyses of pregnancy outcomes in research. Although, AMA

is a risk factor for many adverse pregnancy outcomes, it is not clear whether increased risks of

adverse pregnancy outcomes are due to age-related changes in the uterus or due to other risk

factors. Assessing the combined effect of AMA with other risk factors such as socioeconomic

status on pregnancy outcomes is needed to provide sufficient evidence for maternal practices.

Based on the results of the study, the magnitude of ART risks was greater than other risk factors

for placenta previa and different techniques of ART contributed differently to the risk of placenta

previa. For example, the adverse effect of IVF was stronger than embryo transfer and the strength

of associations were stronger than other studies. Therefore, further research may be needed to

evaluate the effect of different techniques of ART on placenta previa and placenta previa outcomes

in Finnish population.

Placenta previa is a costly complication. Higher risks of adverse maternal outcomes in women

with placenta previa, higher risks of emergency Caesarean section, longer stay in hospital due to

Caesarean section as well as higher risks of preterm birth and NICU admission impose more costs

on families and healthcare systems. From another point of view, AMA increases the length of

maternal and fetal hospitalization and consequently increases both direct and indirect costs. Direct

costs from prolonged maternal hospitalization and indirect cost related prematurity of infants,

maternal complications and cost related to amniocentesis (Haslinger et al. 2016). Therefore, both

placenta previa and pregnancies in older women contribute substantially to adverse pregnancy

outcomes and place an economic burden on families and healthcare systems. Therefore, further

studies are needed to examine the costs related to placenta previa and other risk factors such as

AMA and to explore how costs can be eliminated.

42

8 CONCLUSIONS

The results of this large population based study showed that AMA was an independent risk factor

for placenta previa, increasing the risk of placenta previa by 54%. The results of this thesis

supported the association of placenta previa with IVF, embryo transfer, amniocentesis and

previous Caesarean section.

We hypothesized that placenta previa coupled with age-related changes in the uterus may worsen

pregnancy outcomes. This study addressed an important gap in evidence, to our knowledge, the

effect of maternal age on placenta previa outcomes has not previously assessed. In this regard, the

effect of AMA on placenta previa was presented in the adjusted and unadjusted models. In general,

women with placenta previa were at increased risks of adverse pregnancy outcomes in comparison

to women without placenta previa. The results of the unadjusted model suggested that the risk of

adverse pregnancy outcomes in women with placenta previa was not uniform across maternal age

groups. The results of adjusted models confirmed that the combination of placenta previa and

AMA were associated with slightly increased risks of blood transfusion and placental abruption.

In contrast, younger women with placenta previa had greater risks of preterm birth, NICU

admission, low birth weight and low Apgar score than AMA women with placenta previa.

Overall, the results of this thesis suggested that postponing of childbearing in Finnish mothers

increased the risk of placenta previa and the combination of placenta previa with AMA may have

a marginal effect on the risks of blood transfusion and placental abruption.

43

9 REFRENCES

Abu Heija AT, Jallad MF, Abukteish F. Maternal and perinatal outcome of pregnancies after the

age of 45. Journal of Obstetrics and Gynaecology Research 2000; 26(1):27–30.

Adashek JA, Peaceman AM, Lopez-Zeno JA, Minogue JP, Socol ML. Factors contributing to the

increased cesarean birth rate in older parturient women. Am J Obstet Gynecol 1993;169(4):936–

940.

Ahmed SR, Aitallah A, Abdelghafar HM, Alsammani MA. Major Placenta Previa: Rate, Maternal

and Neonatal Outcomes Experience at a Tertiary Maternity Hospital, Sohag, Egypt: A Prospective

Study. Journal of Clinical and Diagnostic Research 2015;9(11):QC17-QC19.

Albayrak M, Ozdemir I, Koc O, Demiraran Y. Post-partum haemorrhage from the lower uterine

segment secondary to placenta praevia/accreta: Successful conservative management with Foley

balloon tamponade. Australian & New Zealand Journal Of Obstetrics & Gynaecology

2011;51(4):377-380.

Alexandersson O, Bixo M, Hogberg U. Evidence-based changes in term breech delivery practice

in Sweden. Acta Obstet Gynecol Scand 2005;84:584-7.

Allison L, Kearney, Katherine MW. Examining the psychosocial determinants of women's

decisions to delay childbearing. Hum Reprod 2016;31(8):1776-1787.

Ananth CV, Wilcox AJ, Savitz DA, Bowes WA, Luther ER. Effect of maternal age and parity on

the risk of uteroplacentalbleeding disorders in pregnancy. Obstet Gynecol 1996a;88:511–16.

Ananth CV, Savitz DA, Luther ER. Maternal cigarette smoking as a risk factor for placental

abruption, placenta previa, and uterine bleeding in pregnancy. Am J Epidemiol 1996b;144: 881–

889.

44

Ananth CV, Demissie K, Smulian JC, Vintzileos AM. Relationship among placenta previa, fetal

growth restriction, and preterm delivery: a population-based study. Obstet Gynecol 2001;98:299–

306.

Ananth CV, Demissie K, Smulian JC, Vintzileos AM. Placenta previa in singleton and twin births

in the United States, 1989 through 1998: a comparison of risk factor profiles and associated

conditions. Am J Obstet Gynecol 2003a;188:275–8.

Ananth CV, Smulian JC, Vintzileos AM. The effect of placenta previa on neonatal mortality: a

population-based study in the United States, 1989 through 1997. Am J Obstet Gynecol

2003b;188:1299–304.

Andersson G, Rønsen M, Knudsen LB, Lappegård T, Neyer G, Skrede K, Teschner K. Vikat A .

Cohort fertility patterns in the Nordic countries. DEMOGRAPHIC RESEARCH 2009;20:313-

352.

Apgar V. A proposal for a new method of evaluation of the newborn infant. Curr Res Anesth Analg

1953;32(4):260-7.

Bayrampour H, Heaman M: Advanced maternal age and the risk of Cesarean birth: a systematic

review. Birth 2010;37(3):219-226.

Bailey MJ. More power to the pill: the impact of contraceptive freedom on women’s life cycle

labor supply. Q J Econ 2006;121:289–320.

Bao Y, Xu C, Qu X, Quan S, Dong Y, Ying H. Risk factors for transfusion in cesarean section

deliveries at a tertiary hospital. Transfusion 2016:56; 2062–2068.

Bell JS, Campbell DM, Graham WJ, Penney GC, Ryan M, Hall MH. Can obstetric complications

explain the high levels of obstetric interventions and maternity service use among older women?

A retrospective analysis of routinely collected data. BJOG 2001; 108:910–8.

Benirschke K, Kaufmann P. The pathology of the human placenta. New York: Springer-Verlag

45

1990.

Berg CJ, Callaghan WM, Syverson C, Henderson Z. Pregnancy-related mortality in the United

States, 1998 to 2005. Obstetrics & Gynecology 2010;116(6)2010:1302-1309.

Bergmann RL, Richter R, Bergmann KE, Dudenhausen JW. Prevalence and risk factors for early

Post-partum anaemia. Eur J Obstet Gynecol Reprod Biol 2010;150(2):126–31.

Bianchi SM. A Demographic Perspective on Family Change. Journal of family theory & review

2014;6(1):35-44.

Billari FC, Kohler HP. Patterns of low and lowest-low fertility in Europe .Population Studies

2004;58(2): 161-176.

Bin YS, Roberts CL, Nicholl MC, Nassar N, Ford JB. Contribution of changing risk factors to the

trend in breech presentation at term. Aust N Z J Obstet Gynaecol 2016;56: 564–570.

Biro M. A, Davey M.A, Carolan M, Kealy M. Advanced maternal age and obstetric morbidity for

women giving birth in Victoria, Australia: A population-based study. Aust N Z J Obstet Gynaecol

2012; 52:229–234.

Biswas R, Sawhney H, Dass R, Saran RK, Vasishta K. Histopathological study of placental bed

biopsy in placenta previa. Acta obstetricia et gynecologica Scandinavica 1999;78(3):173–9.

Blanc AK, Winfrey W, Ross J. New findings for maternal mortality age patterns: aggregated

results for 38 countries. PLoS One 2013;8(4):e59864.

Blomberg M, Birch Tyrberg R, Kjølhede P. Impact of maternal age on obstetric and neonatal

outcome with emphasis on primiparous adolescents and older women: a Swedish Medical Birth

Register Study. BMJ Open 2014;4(11):e005840.

46

Boivin J, Rice F, Hay D, Harold G, Lewis A, van den Bree MMB, et al. Associations between

maternal older age, family environment and parent and child wellbeing in families using assisted

reproductive techniques to conceive. Soc Sci Med 2009;68:1948–55.

Bonellie S. Effect of maternal age, smoking, and deprivation on birthweight. Paediatric and

Perinatal Epidemiology 2001;15:19-26.

Buehler JW, Kaunitz AM, Hague JR, Hughes JM, Smith JM, Rochat RW. Maternal mortality in

women aged 35 years or older: United States. JAMA 1986; 255: 53-57.

By the EURO-PERISTAT project in collaboration with SCPE, EUROCAT & EURONEOSTAT.

European perinatal health report. EURO-PERISTAT 2004. (Accessed 10 March 2017).

http://www.europeristat.com

Carolan MC, Davey MA, Biro M, Kealy M. Very advanced maternal age and morbidity in

Victoria, Australia: a population based study. BMC Pregnancy and Childbirth 2013;13:80.

Carolan M, Frankowska D. Advanced maternal age and adverse perinatal outcome: a review of

the evidence. Midwifery 2011; 27: 793–801.

Cho JY, Lee YH, Moon MH, Lee JH. Difference in migration of placenta according to the

location and type of placenta previa. J Clin Ultrasound 2008;36:79–84.

Cleary-Goldman J, Malone FD, Vidaver J, Ball RH, Nyberg DA, Comstock CH, Saade GR,

Eddleman KA, Klugman S, Dugoff L, et al. Impact of maternal age on obstetric outcome. Obstet

Gynecol 2005;105(5 Pt 1):983–990.

Coroleu B, Barri PN, Carreras O, Martinez F, Parriego M, Hereter L, Parera N, Veiga A, Balasch

J. The influence of depth of embryo replacement into the uterine cavity on implantation rates after

IVF: A controlled, ultrasound-guided study. Hum Reprod 2002; 17:341-346.

Crane JM, Van den Hof MC, Dodds L, Armson BA, Liston R. Maternal complications with

47

placenta previa. Am J Perinatol 2000;17:101–5.

Crane E, Morris JK. Changes in maternal age in England and Wales - implications for Down

syndrome. Down Syndrome Research and Practice 2006;10(1);41-43.

Cresswell JA, Ronsmans C, Calvert C, Filippi V. Prevalence of placenta previa by world region:

a systematic review and meta-analysis. Trop Med Int Health 2013;18:712–24.

Cunningham FG, MacDonald PC, Gant NF, Leveno KJ, Gilstrap LG. Obstetrical hemorrhage.

Williams obstetrics. New York: McGraw-Hill 2010.

Crane JM, van den Hof MC, Dodds L, Armson BA, Liston R. Neonatal outcomes with placenta

previa. Obstet Gynecol 1999;93:541–544.

Dashe JS, McIntire DD, Ramus RM, Santos-Ramos R, Twickler DM. Persistence of placenta

previa according to gestational age at ultrasound detection. Obstet Gynecol 2002;99:692–7.

Delbaere I, Verstraelen H, Goetgeluk S. Pregnancy outcome in primiparae of advanced maternal

age. Eur J Obstet Gynecol Reprod Biol 2007;135:41–46.

Dunson DB, Colombo B, Baird DD. Changes with age in the level and duration of fertility in the

menstrual cycle. Hum Reprod 2002;17(5):1399-1403.

Eloranta K, Auvinen A. Population attitudes towards research use of health care registries: a

population-based survey in Finland. BMC Medical Ethics 2015;16:48.

Erez O, Novack L, Klaitman V, Erez-Weiss I, Beer-Weisel R, Dukler D, Mazor M. Early preterm

delivery due to placenta previa is an independent risk factor for a subsequent spontaneous preterm

birth. BMC Pregnancy Childbirth 2012;12:82.

Erfani A. Localization of determinants of fertility through measurement adaptations in developing-

country settings: The case of Iran: Comment on “Analysis of economic determinants of fertility

48

in Iran: a multilevel approach.” International Journal of Health Policy and Management

2014;3(7):413-415.

Eichelberger KY, Haeri S, Kessler DC, Swartz A, Herring A, Wolfe HM. Placenta previa in the

second trimester: sonographic and clinical factors associated with its resolution. American

journal of perinatology 2011;28(9):735-740.

Favilli A, Pericoli S, Acanfora M, Bini V, Di Renzo G, Gerli S. Pregnancy outcome in women

aged 40 years or more. Journal Of Maternal-Fetal & Neonatal Medicine 2012;25(8):1260-1263.

Ferré C, Callaghan W, Olson C, Sharma A, Barfield W. Effects of Maternal Age and Age-Specific

Preterm Birth Rates on Overall Preterm Birth Rates - United States, 2007 and 2014.MMWR Morb

Mortal Wkly Rep 2016;65(43):1181-1184.

Fagbamigbe AF, Idemudia ES. Survival analysis and prognostic factors of timing of first childbirth

among women in Nigeria. BMC Pregnancy and Childbirth 2016;16:102.

Fan D, Wu S, Liu L, Xia Q, Wang W, Guo X, Liu Z. Prevalence of antepartum hemorrhage in

women with placenta previa: a systematic review and meta-analysis. Scientific Reports

2017;7:40320.

Fertility statistics. Eurostat 2017. (Accessed 14.03.2017).

http://ec.europa.eu/eurostat/statistics-explained/index.php/Fertility_statistics

Flenady V, Koopmans L, Middleton P et al. Major risk factors for stillbirth in high-income

countries: a systematic review and meta-analysis. Lancet 2011;377:1331–40.

Gilliam M, Rosenberg D, Davis F. The likelihood of placenta previa with greater number of

cesarean deliveries and higher parity. Obstetrics and Gynecology 2002;99(6):976–980.

Gliklich RE, Dreyer NA, Leavy MB. Registries for Evaluating Patient Outcomes. United States:

Agency for Healthcare Research and Quality 2014.

49

Gibbs RS, Danforth DN. Danforth's obstetrics and gynecology. Philadelphia: Lippincott Williams

& Wilkins 2008.

Gissler M, Louhiala P, Hemminki E. Nordic Medical Birth Registers in Epidemiological Research.

European Journal of Epidemiology 1997;13:169–75.

Gissler M, Haukka J. Finnish health and social welfare registers in epidemiological research. Nors

Epidemiologi 2004;14(1):113-120.

Gissler M. Registration of Births and Induced Abortions in the Nordic Countries. Finnish

Yearbook of Population Research 2010. (Accessed 30.03. 2017).

http://journal.fi/fypr/article/view/45059

Gissler M, Mohangoo AD, Blondel B, Chalmers J, Macfarlane A, Gaizauskiene A, Gatt M, Lack

N, Sakkeus L, Zeitlin J.Perinatal health monitoring in Europe: results from the EURO-PERISTAT

project. Inform Health Soc Care 2010;35:64-79.

Goldstein JR, Sobotka T, Jasilioniene A. The end of lower-low fertility? Popul Dev Rev

2009;35:663–700.

Grotegut CA, Chisholm CA, Johnson LNC, Brown HL, Heine RP, James AH. Medical and

Obstetric Complications among Pregnant Women Aged 45 and Older. Young RC, ed. PLoS ONE

2014;9(4):e96237.

Gurol-Urganci I, Cromwell DA, Edozien LC, Smith GC, Onwere C, Mahmood TA, Templeton A,

van der Meulen JH. Risk of placenta previa in second birth after first birth cesarean section: a

population-based study and meta-analysis. BMC Pregnancy Childbirth 2011;11(1):95.

50

Haavaldsen C, Samuelsen SO, Eskild A. The association of maternal age with placental weight: a

population-based study of 536 954 pregnancies. BJOG: An International Journal of Obstetrics &

Gynaecology 2011;118:1470-1476.

Harper LM, Odibo AO, Macones GA, Crane JP, Cahill AG. Effect of Placenta Previa on Fetal

Growth. American journal of obstetrics and gynecology 2010;203:330.e1-330.e5.

Haslinger C,Stoiber B,Capanna F, Schäffer MK, Zimmermann R, Schäffer L. Postponed

pregnancies and risks of very advanced maternal age. Swiss Med Wkly 2016;146:w14330.

Hadfield, L. Motherhood, choice and the British media: A time to reflect. Gender and Education

2007;19(2): 255–63.

Hasegawa J, Matsuoka R, Ichizuka K, Mimura T, Sekizawa A, Farina A, et al. Predisposing factors

for massive hemorrhage during cesarean section in patients with placenta previa. Ultrasound

Obstet Gynecol 2009;34:80–4.

Holzman C, Eyster J, Kleyn M, et al. Maternal Weathering and Risk of Preterm Delivery.

American Journal of Public Health 2009;99(10):1864-1871.

Irgens LM. The Medical Birth Registry of Norway. Epidemiological research and surveillance

throughout 30 years. Acta Obstet Gynecol Scand 2000;79:435-9.

Irgens LM. Challenges to registry-based epidemiology in post-modernistic civilization. Nor

Epidemiol 2001;11:127–31.

Iyasu S, Saftlas AK, Rowley DL, Koonin LM, Lawson HW, Atrash HK. The epidemiology of

placenta previa in the United States, 1979 through 1987. Am J Obstet Gynecol 1993;168:1424–9.

Janoudi G, Kelly S, Yasseen A, Hamam H, Moretti F, Walker M.Factors Associated With

Increased Rates of Caesarean Section in Women of Advanced Maternal Age.J Obstet Gynaecol

51

Can 2015;37(6):517-26.

Jakobsson M, Gissler M, Tapper A-M. Risk factors for blood transfusion at delivery in Finland.

Acta Obstet Gynecol Scand 2013;92:414–420.

Johnston R, Fong A, Lovell S, Sobolewski PS, Rad S, Turner A. Demographic and Obstetric

Outcomes of Pregnancies conceived by Assisted Reproductive Technology (ART) compared to

Non-ART Pregnancies.BRA Assist Reprod 2015;19(1):16-20.

Johnson LG, Mueller BA, Daling JR. The relationship of placenta previa and history of induced

abortion. Int J Gynecol Obstet 2003;81(2):191-8.

Jolly M, Sebire N, Harris J, et al. The risks associated with pregnancy in women aged 35 years or

older. Hum Reprod 2000;15:2433–7.

Joseph KS, Allen AC, Dodds L, Turner LA, Scott H, Liston R.The perinatal effects of delayed

childbearing. Obstet Gynecol 2005;105: 1410–1418.

Katz A. Waiting For Something to Happen: Hospitalization with Placenta Previa. Birth 2001;28:

186–191.

Kenny LC, Lavender T, McNamee R, O’Neill SM, Mills T, Khashan AS. Advanced Maternal

Age and Adverse Pregnancy Outcome: Evidence from a Large Contemporary Cohort. Shi Q, ed.

PLoS ONE 2013;8(2):e56583.

Kramer MS, Dahhou M, Vallerand D, Liston R, Joseph KS. Risk factors for postpartum

hemorrhage: can we explain the recent temporal increase? J Obstet Gynaec Can 2011;33:810–9.

King DL. Placental migration demonstrated by ultrasonography. Radiology 1973;109:167.

Klemetti R, Gissler M, Sainio S, Hemminki E. Associations of maternal age with maternity care

52

use and birth outcomes in primiparous women: a comparison of results in 1991 and 2008 in

Finland. BJOG 2013;121:356–62.

Klemetti R, Gissler M, Sainio S, Hemminki E. At what age does the risk for adverse maternal

and infant outcomes increase? Nationwide register-based study on first births in Finland in

2005–2014. Acta Obstet Gynecol Scand 2016; 95:1368–1375.

Lesthaeghe R, Neels K. From the first to the second demographic transition: an interpretation of

the spatial continuity of demographic innovation in France, Belgium and Switzerland, European

Journal of Population 2002;18(4): 325–360.

Lal AK, Hibbard JU. Placenta Previa: An Outcomes Based Cohort Study in a Contemporary

Obstetrics Population. Archives of Gynecology and Obstetrics 2015;206:S57.

Lamminpää R. Advanced Maternal Age, Pregnancy and Birth. Finland: University of Eastern

Finland 2015.

Lamminpää R, Vehvilainen-Julkunen K, Gissler M, Heinonen S. Preeclampsia complicated by

advanced maternal age: a registry-based study on primiparous women in Finland 1997-2008. BMC

Pregnancy Childbirth 2012: 12:47.

Laopaiboon M, Lumbiganon P, Intarut N, Mori R, Ganchimeg T, Vogel JP, Souza JP, Gülmezoglu

AM. WHO Multicountry Survey on Maternal Newborn Health Research Network Advanced

maternal age and pregnancy outcomes: a multicountry assessment. BJOG 2014;1:49–56.

Lawlor DA, Mortensen L, Andersen AMN. Mechanisms underlying the associations of maternal

age with adverse perinatal outcomes: a sibling study of 264 695 Danish women and their firstborn

offspring. Int J Epidemiol 2011;40 (5):1205-1214.

Lavecchia M, Sabbah M, Abenhaim HA. Effect of Planned Mode of Delivery in Women with

Advanced Maternal Age . Matern Child Health J 2016;20:2318-2327.

53

Lemoine ME, Ravitsky V. Sleepwalking into infertility: The need for a public health approach

toward advanced maternal age. American Journal of Bioethics 2015;15(11):37–48.

Leridon H. Can assisted reproduction technology compensate for the natural decline in fertility

with age? A model assessment.Hum Reprod 2004;19:1548–1553.

Lesthaeghe R. The second demographic transition: A concise overview of its development.

Proceedings of the National Academy of Sciences of the United States of America

2014;111:18112-18115.

Lin H, Xirasagar S. Maternal age and the likelihood of a maternal request for cesarean delivery: a

5-year population-based study. Am J Obstet Gynecol 2005;192:848–55.

Liu X, Zhang W. Effect of maternal age on pregnancy: a retrospective cohort study.Chin Med J

2014;127:2241-2246.

Lisonkova S, Sheps S B, Janssen P A, Lee SK, Dahlgren L. Effect of older maternal age on birth

outcomes in twin pregnancies: a population-based study. J Perinatol 2011;31:85–91.

Ludford I, Scheil W, Tucker G, Grivell R. Pregnancy outcomes for nulliparous women of

advanced maternal age in South Australia, 1998-2008. Aust N Z J Obstet Gynaecol 2012;52:235–

241.

Luke B, Brown MB. Elevated risks of pregnancy complications and adverse outcomes with

increasing maternal age. Hum Reprod 2007;22(5):1264-1272.

Martin SP. Diverging fertility among U.S. women who delay childbearing past age 30.

Demography 2000;37:523–533.

54

Maheshwari A, Porter M, Shetty A, Bhattacharya S. Women’s awareness and perceptions of delay

in childbearing. Fertil Steril 2008;90:1036–42.

Mastrolia SA,Baumfeld Y,Loverro G, Yohai D, Hershkovitz R,Weintraub AY. Placenta previa

associated with severe bleeding leading to hospitalization and delivery: a retrospective population-

based cohort stud. The Journal Of Maternal-Fetal & Neonatal Medicine 2016;29:3467-3471.

Mathews TJ, Hamilton BE. Delayed childbearing: more women are having their first child later in

life. NCHS Data Brief no. 21 2009;21:1-8.

Mathews T, Hamilton B. Mean Age of Mothers is on the Rise: United States, 2000–2014. NCHS

Data Brief 2016; (232):1–8.

Meyer AM, Olshan AF, Green L, Meyer A, Wheeler SB, Basch E, Carpenter WR. Big data for

population-based cancer research: the integrated cancer information and surveillance system. N C

Med J 2014;75(4):265–269.

Miller DA. Is advanced maternal age an independent risk factor for uteroplacental insufficiency?

Am J Obstet Gynecol 2005;192(6):1974–80.

Mills M, Rindfuss RR, McDonald P, te Velde E, on behalf of the ESHRE Reproduction and

Society Task Force. Why do people postpone parenthood? Reasons and social policy incentives.

Human Reproduction Update 2011;17(6):848-860.

Mills T & Lavender T. Advanced maternal age. Obstetrics, Gynaecology and reproductive

medicine 2010;21(4):107-111.

Meleis AI, Sawyer LM, Im E, Messias DKH, Schumacher K. Experiencing transitions: An

emerging middle-range theory. Advances in Nursing Science 2000; 23:12-28.

Moretti F, Merziotis M, Ferraro ZM, Oppenheimer L, Fung Kee Fung K. The Importance of a Late

55

First Trimester Placental Sonogram in Patients at Risk of Abnormal Placentation. Case Reports in

Obstetrics and Gynecology 2014;2014:345-348.

Nilsen ABV, WALDENSTRÖM U, HJELMSTED A, RASMUSSEN S, SCHYTT E.

Characteristics of women who are pregnant with their first baby at an advanced age. Acta

Obstetricia et Gynecologica Scandinavica 2012;91(3):353-362.

Nørgaard M, Wogelius P, Pedersen L, Rothman KJ, Sorensen HT. Maternal use of oral

contraceptives during early pregnancy and risk of hypospadias in male offspring. Urology

2009;74:583–7.

Nørgaard LN, Pinborg A, Lidegaard Ø, Bergholt T. A Danish national cohort study on neonatal

outcome in singleton pregnancies with placenta previa. Acta Obstet Gynecol Scand 2012;91:546–

51.

Olsen J. Register-based research: Some methodological considerations. Scand. J. Public Health

2011;39:225–9.

Oya A, Nakai A, Miyake H. et al. Risk factors for peripartum blood transfusion in women with

placenta previa: a retrospective analysis. J Nippon Med Sch. 2008;75:146–51.

Oyelese Y, Smulian JC. Placenta previa, placenta accrete, and vasa previa. Obstet Gynecol

2006;107:927–941.

Paulson RJ, Boostanfar R, Saadat P, Mor E, Tourgeman DE, Slater CC, Francis MM, Jain JK.

Pregnancy in the sixth decade of life: Obstetric outcomes in women of advanced reproductive age.

JAMA 2002;288:2320-2323.

Phadungkiatwattana P, Rujivejpongsathron J, Tunsatit T, Yanase Y. Analyzing pregnancy

outcomes in women of extremely advanced maternal age (> or=45 years) J Med Assoc Thai

2014;97(1):1–6.

56

Pirhonen J, Bergersen TK, Abdlenoor M, Dubiel M, Gudmundsson S. Effect of maternal age on

uterine flow impedance. J Clin Ultrasound 2005;33:14–7.

Räisänen S, Kancherla V, Kramer MR, Gissler M, Heinonen S. Placenta Previa and the Risk of

Delivering a Small-for-Gestational-Age Newborn. Obstetrics and gynecology 2014;124:285-291.

Räisänen S. Obstetric Anal Sphincter Ruptures - Risk Factors, Trends and Differences Between

Hospitals. Finland: University of Eastern Finland 2011.

Regushevskaya R, Hemminki E, Klemetti R, Rotkirch A, Karro H, Haavio-Mannila E, Miettinen

A. Postponing Births – Comparing Reasons Among Women in St Petersburg, Estonia and

Finland. Finnish Yearbook of Population Research 2014; 48:127-145.

Romundstad LB, Rommundstad PR, Sunde A, von Düring V, Skjærven R, Vatten LJ. Increased

risk of placenta previa in pregnancies following IVF/ICSI; A comparison of ART and non-ART

pregnancies in the same mother. Hum Reprod 2006;21:2353-2358.

Rosenberg T, Pariente G, Sergienko R, Wiznitzer A,Sheine E.Critical analysis of risk factors and

outcome of placenta previa. Archives of Gynecology and Obstetrics 2011:284;47-51.

Rothman KJ. Epidemiology—an introduction. Oxford: Oxford University Press 2002.

Rothman K. J, Greenland S, Lash, T. L. Modern Epidemiology, 3rd Edition. Philadelphia. 2008.

Rubod C, Deruelle P, Le Goueff F, Tunez V, Fournier M, Subtil D. Long-term prognosis for infants

after massive fetomaternal hemorrhage. Obstet Gynecol 2007;110:256–60.

Saadat S, Chowdhury SA, Mehryar A. Fertility decline in the Islamic Republic of Iran 1980-2006:

a case study. World Bank 2010. (Accessed 08. 03.2017).

http://documents.worldbank.org/curated/en/316231468340135221/Fertility-decline-in-the-

Islamic-Republic-of-Iran-1980-2006-a-case-study

57

Salihu HM, Li Q, Rouse DJ, Alexander GR. Placenta previa: neonatal death after live births in the

United States. Am J Obstet Gynecol 2003;188(5):1305–1309.

Seffah JD. Maternal and perinatal mortality and morbidity associated with transverse lie.

International Journal of Gynecology and Obstetrics 1999;65(1):11–15.

Sekiguchi A, Nakai A, Kawabata I, Hayashi M, Takeshita T. Type and Location of Placenta Previa

Affect Preterm Delivery Risk Related to Antepartum Hemorrhage. International Journal of

Medical Sciences 2013;10(12):1683-1688.

Silver RM, Landon MB, Rouse DJ, Leveno KJ, Spong CY, Thom EA. et al. Maternal morbidity

associated with multiple repeat cesarean deliveries. Obstetrics & Gynecology 2006;107(6):1226–

1232.

Simpson R. ‘Assisted Reproductive Technologies’. Parleman of New South Wales 1998.

(Accessed 08.03.2017).

https://www.parliament.nsw.gov.au/researchpapers/Documents/assisted-reproductive-

technology/bp06-98.pdf

Schmidt L, Sobotka T, Bentzen JG, Nyboe Andersen A; on behalf of the ESHRE Reproduction

and Society Task Force. Demographic and medical consequences of the postponement of

parenthood. Hum Reprod Update 2012;18:29–43.

Schisterman EF, Cole SR, Platt RW. Overadjustment Bias and Unnecessary Adjustment in

Epidemiologic Studies. Epidemiology 2009;20(4):488-95.

Sheiner E, Shoham-Vardi I, Hallak M, Hershkowitz R, Katz M, Mazor M. Placenta previa:

Obstetric risk factors and pregnancy outcome. Journal of Maternal Fetal Medicine

2001;10(6):414–419.

58

Sheiner E, Shoham-Vardi I, Hallak M, Hadar A, Gortzak- Uzan L, Katz M, Mazor M. Placental

abruption in term pregnancies: clinical significance and obstetric risk factors. J Matern Fetal

Neonatal Med 2003;13:45–49.

Smith GCS, Cordeaux Y, White IR, Pasupathy D, Missfelder-Lobos H, Pell JP, FlemingM. The

Effect of Delaying Childbirth on Primary Cesarean Section Rates. PLoS Medicine 2008;5(7),

e144.

Stein Z, Susser M. The risks of having children in later life : Social advantage may make up for

biological disadvantage. BMJ : British Medical Journal 2000;320(7251):1681-1682.

Strong TH, Brar HS. Placenta previa in twin gestations. J Reprod Med 1989 Jun;34(6):415-6.

Taipale P, Hiilesmaa V, Ylostalo P. Transvaginal ultrasonography at 18–23 weeks in predicting

placenta previa at delivery. Ultrasound Obstet Gynecol 1998;12(6): 422–425.

The National Institute for Health and Welfare (THL) 2017. (Accessed 10.01.2017).

www.thl.fi/en/statistics/parturients.

The National Institute for Health and Welfare (THL) 2015a. (Accessed 12.03.2017).

https://www.thl.fi/en/web/thlfi-en/statistics/statistics-by-topic/sexual-and-reproductive-

health/assisted-fertility-treatments

The National Institute for Health and Welfare (THL) 2015b. (Accessed 01.12.2016).

https://www.thl.fi/en/web/thlfi-en/statistics/information-on-statistics/register-descriptions/care-

register-for-health-care.

The National Institute for Health and Welfare (THL) 2015c. (Accessed 1 Dec 2016).

https://www.thl.fi/fi/web/thlfi-en/statistics/information-on-statistics/register-

descriptions/register-of-congenital-malformations.

59

Tikkanen M, Nuutila M, Hiilesmaa V, Paavonen J, Ylikorkala O. Clinical presentation and risk

factors of placental abruption. Acta Obstet Gynecol Scand 2006;85(6):700–705.

Usta IM, Nassar AH. Advanced maternal age. Part I: obstetric complications. Am J Perinatol

2008;25:521–34.

The Health and care of pregnant women and babies in Europe in 2010. European Perinatal Health

Report. EURO-PERISTAT 2013. (Accessed 10.01.2017).

http://www.europeristat.com.

Thygesen LC, Ersbøll AK. When the entire population is the sample: strengths and limitations in

register-based epidemiology. Eur J Epidemiol 2014;29(8):551–8.

Vahanian S.A, Lavery JA, Ananth CV, Vintzileos A. Placental implantation abnormalities and risk

of preterm delivery: a systematic review and met analysis. Am J Obstetr Gynecol 2015;213;S78–

S90.

Van de Kaa DJ. Europe’s Second Demographic Transition. Population Bulletin 1987;42(1):1–59.

Vikat A. “Women’s labor force attachment and childbearing in Finland.” Demographic Research

Special Collection 3 2004;8: 177-212.

Walfisch A & Sheiner E. Placenta previa and immediate outcome of the term offspring. Arch

Gynecol Obstet 2016; 294:739–744.

Walker KF, Bugg GJ, Macpherson M, McCormick C, Grace N, Wildsmith C, Bradshaw L, Smith

GC, Thornton JG. Randomized Trial of Labor Induction in Women 35 Years of Age or Older. N

Engl J Med 2016;374(9):813-22.

60

Waldenström U, Aasheim V, Nilsen ABV, Rasmussen S, Pettersson HJ, Schytt E. Adverse

Pregnancy Outcomes related to Advanced Maternal Age Compared With Smoking and Being

Overweight. Obstetrics & Gynecology 2014;123(1):104-12.

Weiss NS. The new world of data linkages in clinical epidemiology: are we being brave or

foolhardy? Epidemiology 2011;22:292–4.

Wennberg AL, Opdahl S, Bergh C, Aaris Henningsen AK, Gissler M, Romundstad LB, Pinborg

A, Tiitinen A, Skjærven R, Wennerholm UB. Effect of maternal age on maternal and neonatal

outcomes after assisted reproductive technology. Fertil Steril 2016;106(5):1142-1149.

Wilcox AJ. The medical birth registry of Norway – an international perspective. Norsk

Epidemiolog 2007;17:103–105.

Williams MA, Mittendorf R, Lieberman E, Monson RR, Schoenbaum SC, Genest DR. Cigarette

smoking during pregnancy in relation to placenta previa. Am J Obstet Gynecol 1991;165(1):28–

32.

Wolff KM, McMahon MJ, Kuller JA, Walmer DK, Meyer WR. Advanced maternal age and

perinatal outcome: oocyte recipiency versus natural conception. Obstet Gynecol 1997;89(4):519–

23.

Wong TY. Emergency peripartum hysterectomy: a 10-year review in a tertiary obstetric hospital.

N Z Med J 2011;124(1345):34-9.

Xiaoli L, Weiyuan Z. Effect of maternal age on pregnancy: a retrospective cohort study. Chinese

Medical Journal 2014;127(12):2241-2246.

Zlatnik M, Cheng Y, Norton M, Thiet M, Caughey A. Placenta previa and the risk of preterm

delivery. J Matern Neonatal Me 2007;20:719–23.

61