Registries rolv skjaerven

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Unique Epidemiologic Research
Rolv Skjærven
Department of Global Public Health and Primary Care
and
Norwegian Institute of Public Health, Bergen
Unique Epidemiologic Research

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The importance of linked data
By “linked”, epidemiologists mean a birth record that can be
connected to other births from the same woman, or to other
records for the same baby.
To appreciate the importance of linked data, we should
first consider unlinked birth data. Much of what we know about
infant mortality has come from birth certificates collected as
part of vital statistics.
Most countries have laws that require collection of vital
statistics, including legal records of births and deaths. These
birth certificates typically exist in isolation, without being
linkable to other deliveries by the same woman, or to later
health problems occurring to that baby.
AJ Wilcox, 2007

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THE INTERNATIONAL IMPACT OF THE
MEDICAL BIRTH REGISTRY OF NORWAY
In 1995, the US Centers for Disease Control and the US
National Institutes of Health organized an international
symposium on maternally-linked pregnancy outcomes.
Along with the excellent linked Registries of Sweden and
Denmark, the Medical Birth Registry of Norway was one
of the centerpieces of that symposium. Ten years later, in
2005, a second international symposium was held, and
once again, Norwegian researchers played a prominent
role.
AJ Wilcox, 2007

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Medical birth registries started:
Norway – 1967
Iceland – 1972
Sweden/Denmark – 1973
Finland – 1987
Number of infants born (2011):
61322 (N) 4480 (I) 109766 (S)
59666 (D) 60258 (F)
How to do excellent research?
Topic for next years
EPINOR summer school

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6. 06.10.2016
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Promoting innovation
and creativity in epidemiology
Hiatt et al., Anals of Epidemiol,2013
New and innovative thinking will be
needed to develop and apply
methods to empirical observation that
complement our traditional
approaches to hypothesis-driven research
and making causal inferences.
I think there is true inspiration and there is synthesis. They are not mutually
exclusive, they need to be paired.
One can collect data - observations - like Darwin did for months and
months, on end classify, look for patterns and structure; but all the time the
underlying direction of the synthesis is being guided by inspiration - a half
understood idea, something you have 'received' in a dream or by relaxing.
In effect one side is sheer hard work and intellectuallydriven
'bottom up' analysis and the other is subconscious emotion and relaxation
driven 'top down' inspiration.
For one you need reason and memory; for the other you need to
cut out reason and memory because they get in the way.
For one you need a working environment, an office, files,
computers, structure, for the other you need nature, play, music,
unstructured environments and meditation.
Where good ideas come from
Rosemary Rock-Evans, 2014, comment to
Steven Johnson’s Where good ideas come from

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During the last 20 years, more than 20 papers have
been published in the leading weekly clinical journals
– the most influential journals in the field of
medicine and public health.
Thirty papers have been published in the international
specialty clinical journals in pediatrics, obstetrics and
other fields,
and nearly 80 papers have appeared in major
epidemiology journals.
Norsk epidemiologi, 2007
The Medical Birth Registry of Norway –
An International Perspective. AJ Wilcox
The leading weekly
clinical journals
We have now as many publications (40)
as Sweden in epidemiologic research
based on the
Medical Birth Registry
of Norway and Sweden, resp.

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Overall, we have a third of
Sweden’s publications in
N Engl J Med, BMJ, Lancet, JAMA
…but
The leading weekly
clinical journals
We have unique
registry data in Norway
Go for the unique aspects
in the data!

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Family data is a
unique «factor»
A narrow area for science,
but these data dominate
our «top» papers

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In relation to a pregnancy,
it will be wrong to say
that the risk for a specific pregnancy
condition is X%
Most often risks are higher in 1st pregnancies,
and risks in later pregnancies depends on
what happened in previous pregnancies.
Some examples …

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06/10/2016 21
Perinatal mortality and continuation rates
by outcome of previous births (1967-98)
Birth Order
1 2 3 4
Perinatalmortality(per1000)
20
30
40
50
60
70
80
90
200
300
10
100
Surviving child
Perinatal loss
78
70
84
72
62
64
36
60
33
64
21
72
79
37
Continuation rate
Update of earlier studies:
Skjaerven et al.,
1987, PPE & 1988, AJE
06/10/2016 22
Risk for preeclampsia, 1st to 4th birth, by outcome of previous births.
Singleton births, same partner for all births, Norway 1967-2009
Birth order
1 2 3 4
PerCent
0
5
10
15
20
25
30
35
40
45
Conclusions:
After one preeclamptic pregnancy,
the risk for the next pregnancy is
between 10 and 15 %,
regardless of birth order.
After two or more
preeclamptic pregnancies,
the risk is between 30 and 40%.
Red lines:
risk following
preeclampsia
Skjaerven et al. ‘The epidemiology of
preeclampsia with focus on family data’
In ‘Placental Bed Disorders’,
Pijnenborg et al. (eds), 2011

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Generations and recurrence
Breech delivery (BMJ; 2008)
Preeclampsia (BMJ; 2005)
Malformations (NEJM, 1994 & 1999, JAMA 2001)
Breech deliveries and generations
• Hypothesis: Both women and men
delivered in breech presentation
contribute to increased risk of breech
delivery in their offspring ??
• ”Inherited”
from women (OR=2.2;1.9-2.5)
from men ?? (OR=2.2; 1.8-2.7)
Nordtveit et al.,BMJ, 2008

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Breech deliveries and generations
• Hypothesis: Both women and men
delivered in breech presentation
contribute to increased risk of breech
delivery in their offspring
• ”Inherited” as strongly
from men (OR=2.2; 1.8-2.7) as
from women (OR=2.2;1.9-2.5)
Nordtveit et al.,BMJ, 2008
Figure 1. Risk for preeclampsia in the second generation, given a
preeclamptic pregnancy in the first generation. (Shaded area
represents preeclamptic pregnancies.)
(Skjaerven et al., BMJ, 2005)

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Recurrence of malformations
from one generation to the next
• Focus the recurrence of similar or dissimilar defects
(25 categories of defects)
– Sibling recurrence
(Lie, Wilcox, Skjaerven; NEJM, 1994)
– Malformations, mother to offspring
(Skjaerven, Wilcox, Lie; NEJM, 1999)
– Malformations, father to offspring
(Lie, Wilcox, Skjaerven; JAMA, 2001)
Recurrence of malformations:
siblings and generations
Recurrence Risks
Total Same Different
• 1st to 2nd siblings: 2.5 7.6 1.7
• mother to offspring: 1.6 6.8 1.0
• father to offspring: 2.4 6.5 1.8
Attributable risks:
– Affected mothers contribute to 5 out of 1000 registered
birth defects in the next generation
– Affected fathers contribute to 16 out of 1000

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Preeclampsia risk increases with pregnancy
interval.
Implications for the observed effects of parity
and paternity.
Reference:
Skjaerven, Wilcox, Lie, NEnglJMed, 2002
Pregnancy interval (years)
Proportion
ofbirths(%)
0
10
20
30
40
50
60
70
80
90
100
110
120
130
140
1 2 3 4 5 6 7 8 9 10
Preeclampsiarisk(%)
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
RISK AT FIRST PREGNANCY
Preeclampsia
risk in 2nd
pregnancy
OVERALL RISK
AT SECOND PREGNANCY

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Preeclampsia risk in 2nd pregnancy,
by interval since the 1st pregnancy
for mothers with the same partner and different partner
Pregnancy interval (years)
1 2 3 4 5 6 7 8 9 10
Preeclampsia(%)
0,0
0,5
1,0
1,5
2,0
2,5
3,0
SAME
PARTNERS
DIFFERENT
PARTNERS
Median pregnancy interval
for 2nd births by
- different fathers
- same father
Distance (complete years)
Numberofbirths(%)
0
10
20
30
Preeclampsia by distance between pregnancies,
women with same and new partner in 2nd pregnancies
Distance is calculated from date of birth to date of conseption of next pregnancy
0 2 4 6 8 10 12 14
PreeclampsiaRisk(%)
0.7
0.8
0.9
1.5
2
3
4
5
1

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Odds Ratio of preeclampsia for mothers who had changed partner
between two pregnancies, and mothers who had the same partner
for both pregnancies, for different scenarios of adjustment for
potentially confounding factors.
2nd pregnancy 3rd pregnancy 4th pregnancy
1) Crude estimate 1.3 (1.2-1.5) 1.4 (1.2-1.5) 1.6 (1.2-2.2)
2) Adjusted for smoking
during pregnancy (*) 1.5 (1.3-1.7) 1.5 (1.2-1.8) 1.8 (1.3-2.6)
3) Adjusted for
inter-birth interval 0.8 (0.7-0.9) 0.9 (0.8-1.2) 0.9 (0.6-1.3)
4) Adjusted for interval
and smoking 0.9 (0.8-1.1) 1.0 (0.8-1.2) 1.0 (0.7-1.5)
5) Adjusted for interval,
smoking and maternal
age at last birth 0.9 (0.8-1.1) 1.0 (0.8-1.3) 1.0 (0.6-1.5)
(*) daily smoking versus no smoking
Odds Ratios with 95% C.I.
Conclusions
• Change of partner does not influence risk for
preeclampsia in the next pregnancy
• The “primipaternity” effect is due to confounding
by interval and smoking

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Main results:
Low birthweight, preterm birth and perinatal death
all increased 2.0-2.5 fold, in 2nd birth
for women with a new partner.
Perinatal death in relation to
mother’s and father’s
gestational age and birthweight
Skjaerven et al., BMJ, 1997

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Perinatal death by maternal birthweight
Maternal birthweight (lower cutp)
1000 1500 2000 2500 3000 3500 4000 4500 5000
RR(95%C.I.)
0.8
0.9
1.5
2
3
4
5
1
Skjærven et al., BMJ, 1997 and Nordtveit et al., PPE, 2010
Perinatal death by parental birthweight
Maternal birthweight (lower cutp)
1000
1500
2000
2500
3000
3500
4000
4500
5000
RR(95%C.I.)
0.8
0.9
1.5
2
3
4
5
1
Paternal birthweight (lower cutp)
1000
1500
2000
2500
3000
3500
4000
4500
5000
RR(95%C.I.)
0.8
0.9
1.5
2
3
4
5
1
Mother Father
Nordtveit et al., 2010

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Tandberg et al.; BJOG 2011
Perinatal death in twins and singletons
by maternal gestational age
Perinatal death in low birth weight (<2500g)
by maternal birthweight
Maternal birthweight (lower cutp)
1000 1500 2000 2500 3000 3500 4000 4500 5000
RR(95%C.I.)
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.5
2
3
1
Skjærven et al., BMJ 1989 (update 2011)SSkjaerven et al.,1997 (reanalyzed)

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Preeclampsia and maternal mortality
- Evaluating risk for unaffected sisters
Bjørn Egil Vikse et al. ,
2012, Clin J Am Soc Nephrol, & 2008, N Engl J Med, 2008
Unaffected sisters?
Based on population data,
more than 8 million individuals,
we can find sisters to mothers in MBR
- Assuming that sisters carry similar underlying risk
of chronic disease, this hypothesis will shed light on
whether preeclampsia changes the mortality risk
for a woman, or is simply an indicator of risk
irrespective of the pregnancy condition.

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Preeclampsia and
end stage renal disease:
HR(adj) =5.9 (4.3-8.0)
Unaffected sisters:
HR(adj) =1.01 (0.62-1.7)
Bjørn Egil Vikse et al. ,
2012, Clin J Am Soc Nephrol, & 2008, N Engl J Med, 2008
An example
The Barker Hypothesis
Early origin of adult diseases

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Birthweight and smoking as an adult
Early origin of adult diseases
or
confounding due to social factors?
Kvalvik et al., PPE, 2015
The Barker Hypothesis

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06/10/2016 47
Errors in gestational age
Am.J.Publ.Health, 89, 213-218, 1999
O bserved birth w eight
at 32 w eeks gestational age
B irthw eight (rounded 100 gr)
0 500 1000 1500 2000 2500 3000 3500 4000 4500
PerCent
0
1
2
3
4
5
6
7
8
9
10
11
In clu ded
Exclu ded
3 2 w e eks
preterm -32w distr1
T o tal: M ean =2088 SD =750 n =5129
In clu d ed : M ean =1802 S D =447 n =4154

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Errors in
gestational
age
50

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Pregnancy complications and
long term maternal death
due to cardiovascular causes
The Medical Birth Registry of Norway
• All births in Norway 1967-2016
- 50 years with population based birth registration
• Close to 3 million births, for 1.6 million women
• Personal identifiers for mother, father and child
• Linked to - Death registry, Cancer registry
- Population registry
- Education registry
• Internal linkage - Siblings and Generations

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State of the art?
Pregnancy complication in 1st birth
CVD mortality
Pregnancy complications and
long term maternal death
BMJ 2007, Meta-analysis, Bellamy et al.
The relative risks (95% confidenceintervals)
for ischaemic heart disease 2.2 (1.9 to 2.5) after 12 years,
for stroke 1.8 (1.5 to 2.3) after 10 years
Almost all studies have been based on
preeclampsia in 1st pregnancy
and no focus on effects of later reproduction
Cardiovascular deaths and preeclampsia

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Risk for preeclampsia, 1st to 4th singleton birth,
by preeclampsia in previous births, Norway 1967-2014

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Fig 1 Cumulative risk of
cardiovascular death for
women according to
pre-eclampsia status at
first pregnancy and
number of subsequent
lifetime pregnancies
Cumulativerisk of cardiovasculardeath in strataof outcome of first pregnancy.
In each panel we comparemortality for women with one lifetime birth (green) ,
and women with two or more lifetime births (red) .
HR were adjusted for maternalage, year of first birth and maternaleducation

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Maternal deaths by lifetime number of pregnancies
and term/preterm preeclampsia in first singleton pregnancy.
CARDIOVASCULAR DEATHS (CHD and Stroke, combined)
LIFETIME NUMBER OF
PREGNANCIES
NUMBER
OF WOMEN
MATERNAL
DEATHS (per
1000)
HR (95%C.I.)
Adj. for maternal age,
1st pregnancy
HR (95%C.I.)
ADJUSTED (*)
Women with one pregnancy
0 No preeclampsia 112208 596 (5.3) 2.1 (1.9-2.3) 2.0 (1.8-2.2)
1 Term
preeclampsia
4758 47 (9.9) 4.1 (3.1-5.5) 3.4 (2.6-4.6)
2 Preterm
preeclampsia
1426 29 (20.3) 10.6 (7.3-15.3) 9.4 (6.5-13.7)
Women with two or more pregnancies
3 No preeclampsia 599973 1415 (2.4) 1.0 (ref.) 1.0 (ref.)
4 Term
preeclampsia
21950 68 (3.1) 1.5 (1.2-2.0) 1.5 (1.2-2.0)
5 Preterm
preeclampsia
4459 16 (3.6) 2.3 (1.4-3.8) 2.4 (1.5-3.9)
(*) HR=Hazard Ratio with 95% confidence interval, adjusted for maternal education , maternal age and year of 1st birth
Paternal deaths by lifetime number of pregnancies
and term/preterm preeclampsia in first singleton pregnancy.
CARDIOVASCULAR DEATHS
(CHD and Stroke, combined)
LIFETIME NUMBER OF PREGNANCIES HR (95%C.I.) ADJUSTED (*)
Men whose partner had one pregnancy
0 No preeclampsia 1.7 (1.6-1.7)
1 Term preeclampsia 1.6 (1.4-1.8)
2 Preterm preeclampsia 1.6 (1.2-2.1)
Men whose partner had two or more pregnancies
3 No preeclampsia 1.0 (ref.)
4
Term preeclampsia 0.99 (0.91-1.1)
5
Preterm preeclampsia 0.94 (0.75-1.2)
(*) HR=Hazard Ratio with 95% confidence interval, adjusted for paternal education , paternal age and year of 1st birth

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- no preeclampsia
20-39 40-49 50-59 60-69
0,5
0,6
0,8
1,5
2
3
4
5
6
8
15
20
30
40
1
10
- preterm preeclampsia
20-39 40-49 50-59 60-69
HAZARDRATIO(95%C.I.)
0,5
0,6
0,8
1,5
2
3
4
5
6
8
15
20
30
40
1
10
- term preeclampsia
20-39 40-49 50-59 60-69
0,5
0,6
0,8
1,5
2
3
4
5
6
8
15
20
30
40
1
10
MATERNAL AGE AT DEATH
20-39 40-49 50-59 60-69
HAZARDRATIO(95%C.I.)
0,5
0,6
0,8
1,5
2
3
4
5
6
8
15
20
30
40
1
10
MATERNAL AGE AT DEATH
20-39 40-49 50-59 60-69
0,5
0,6
0,8
1,5
2
3
4
5
6
8
15
20
30
40
1
10
MATERNAL AGE AT DEATH
20-39 40-49 50-59 60-69
0,5
0,6
0,8
1,5
2
3
4
5
6
8
15
20
30
40
1
10
ONE PREGNANCY
2+ PREGNANCIES
HAZARD RATIOS FOR MATERNAL DEATH (CVD)
BY AGE-CATEGORIES AT DEATH
references
Gestational diabetes in 2nd pregnancy for women with
preeclampsia in 1st, by gestational age and z-score
of birthweight by gestational age in 1st, relative to women
with no preeclampsia (week 39-42);
excluding women with diabetes in 1st pregnancy.
Gestational week, 1st pregnancy
25-34 35-36 37-38 39-42
OR(95%C.I.)
0,7
0,85
1,5
2
3
4
5
6
7
8,5
15
1
10
z <= median
z > median

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The GOS syndrome
(great obstetric syndrome)
7 factors:
Preeclampsia, Preterm Birth, Perinatal
Death, Gestational Diabetes, Placental
abruption, Fetal Growth Retardation,
Macrosomia
… and all siblings
These factors are interrelated, and they all have
an effect on maternallongterm survival

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GOS syndrome and maternal CVD death
Total number of complications
0 1 2 3 4 5 6
HazardRatio
0.8
1.5
2
3
4
5
6
8
15
1
10
1 pregn.
2 pregn.
3 pregn.
4 pregn.
Ref.
Figure 7: Parity specific GOS risk by history
of GOS, 1st birth in 1967-2002, followed to 2014
(solid=yes; non-solid=no)
Birth order
1 2 3 4
PerCent
15
20
25
30
35
40
45
50
55
60
65

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Preeclampsia and paradoxes:
• Smoking reduce the occurrence of preeclampsia
(to almost the half for heavy smokers)
• Breast cancer is reduced 20-30%

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Conclusions
Pregnancy complications
and maternal longterm disease or death?
• Not only preeclampsia
• Not only 1st pregnancy
• Not only CVD mortality
the end