Health-Related Quality of Life for Extremely Low Birth Weight Adolescents in
Canada, Germany, and the Netherlands
Erik Verrips, Ton Vogels, Saroj Saigal, Dieter Wolke, Renate Meyer, Lorraine Hoult
and S. Pauline Verloove-Vanhorick
Pediatrics 2008;122;556-561
DOI: 10.1542/peds.2007-1043
The online version of this article, along with updated information and services, is
located on the World Wide Web at:
http://www.pediatrics.org/cgi/content/full/122/3/556
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ARTICLE
Health-Related Quality of Life for Extremely Low
Birth Weight Adolescents in Canada, Germany, and
the Netherlands
Erik Verrips, PhDa,b, Ton Vogels, PhDa, Saroj Saigal, MDc, Dieter Wolke, PhDd, Renate Meyer, PhDe, Lorraine Hoult, BAc,
S. Pauline Verloove-Vanhorick, MD, PhDa,f
a
Applied Scientific Research (TNO) Prevention and Health, Leiden, Netherlands; bAcademic Centre Dentistry Amsterdam, Amsterdam, Netherlands; cDepartment of
Pediatrics, McMaster University, Hamilton, Canada; dDepartment of Developmental Psychology, University of Warwick, Coventry, United Kingdom; eUniversity of Munich
Children’s Hospital, Munich, Germany; fDepartment of Pediatrics, Leiden University Medical Center, Leiden, Netherlands
The authors have indicated they have no financial relationships relevant to this article to disclose.
What’s Known on This Subject
What This Study Adds
Health-related quality of life in adolescence may be affected by ELBW, compared with
normal birth weight.
Differences found in health-related quality of life in 3 countries may affect the functioning of ELBW children later in life. It is important to evaluate the effects of ELBW in
different countries, because findings may not be easily generalized.
ABSTRACT
OBJECTIVE. The goal was to compare health-related quality of life of 12- to 16-year-old
adolescents born at an extremely low birth weight in regional cohorts from Ontario
(Canada), Bavaria (Germany), and the Netherlands.
METHODS. Patients were extremely low birth weight survivors from Canada, Germany,
and the Netherlands. Health-related quality of life was assessed with Health Utilities
Index 3. Missing data were substituted by proxy reports. Differences in mean Health
Utilities Index 3 scores were tested by using analysis of variance. Differences in the
numbers of children with affected attributes were tested by using logistic regression
analyses.
RESULTS. Survival rates were similar; response rates varied between 71% and 90%.
Significant differences in health-related quality of life were found between the
cohorts, with Dutch children scoring highest on Health Utilities Index 3 and German
children scoring lowest, independent of birth weight, gestational age, and cerebral
palsy. Differences in mean utility scores were mainly attributable to differences in the
cognition health attribute. Most of the results were corroborated by logistic regression analyses.
CONCLUSIONS. There were significant differences between the 3 cohorts in health-
related quality of life, not related to differences in birth weight, gestational age, or
cerebral palsy. Survival and response rates alone cannot explain these differences.
Pediatrics 2008;122:556–561
www.pediatrics.org/cgi/doi/10.1542/
peds.2007-1043
doi:10.1542/peds.2007-1043
Key Words
cross-cultural, extremely low birth weight,
health-related quality of life
Abbreviations
CP— cerebral palsy
ELBW— extremely low birth weight
HRQL— health-related quality of life
HUI3—Health Utility Index 3
MAU—multiple-attribute utility
SAU—single-attribute utility
Accepted for publication Dec 14, 2007
Address correspondence to Erik Verrips, PhD,
TNO Prevention and Health, PO Box 2215,
2301 CE Leiden, Netherlands. E-mail: erik.
verrips@tno.nl
PEDIATRICS (ISSN Numbers: Print, 0031-4005;
Online, 1098-4275). Copyright © 2008 by the
American Academy of Pediatrics
F
OLLOW-UP STUDIES OF children and adolescents born at extremely low birth weight (ELBW) (⬍1000 g) have
identified major disabilities such as cerebral palsy (CP), blindness, deafness, and physical growth problems.1,2 In
addition, several studies of children and adolescents have shown substantial levels of cognitive,3–8 affective, and
behavioral9–11 problems, which are even more frequent than physical disabilities.12,13 Serious psychological problems
become more apparent later in adolescence.1
Recently, the importance of self-reported outcomes such as health-related quality of life (HRQL) has been
increasingly recognized, not only for determining the perspective of patients but also for evaluating treatment options
for individual patients and assessing the economic consequences of specific treatments. Such measures have been
used in the study of neonatal care for low birth weight and/or preterm children.14–16 Although a substantial
proportion of children who were born at ELBW reported their HRQL to be within the reference range (despite the
presence of disabilities), a significant minority had lower HRQL than matched reference subjects.
Treatment regimens for ELBW children at birth show substantial differences between countries.17 However, there
is little evidence that these differences have an impact on long-term physical and mental outcomes.18,19 For example,
Hille et al10 did not find differences in patterns of behavioral and emotional problems in different countries, although
levels of intensity differed somewhat.
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VERRIPS et al
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It is not known whether HRQL among ELBW children differs between countries. We investigated the
HRQL of ELBW adolescents in 3 population-based cohorts from central-west Ontario (Canada), Bavaria (Germany), and the Netherlands, in relation to birth weight
and gestational age20 and the prevalence of CP.21 Follow-up outcomes in the 3 cohorts were studied at older
ages22–25 than reported in our present study, but the
outcome measures at those ages were not comparable
between the cohorts.
METHODS
Study Cohorts
Data on HRQL were obtained for all survivors in large,
geographically defined, prospective cohorts of preterm/
very low birth weight infants in 3 countries. These cohorts were (1) the McMaster ELBW Cohort from central-west Ontario (397 births at ⱕ1000 g, born between
1977 and 1982), (2) the Bavarian Longitudinal Study of
Children at Biological Risk from Bavaria (682 births at
⬍1500 g and/or ⬍32 weeks of gestation, born between
1985 and 1986), and (3) the national cohort of the
Dutch Project on Preterm and Small for Gestational Age
Infants (1338 births at ⬍1500 g and/or ⬍32 weeks of
gestation, born during 1983). These cohorts were described extensively elsewhere.1,10,17,21 For the purposes of
the present study, only children born at ⱕ1000 g were
included in the analyses.
All assessments of the Dutch children were performed
some months after the children’s 14th birthday. For the
children in Ontario, the mean age at assessment was
slightly more than 14 years, although the range was
12 to 16 years. The Bavarian children were assessed at
age 13.
Data Collection
HRQL was assessed by using Health Utility Index 3
(HUI3),26 a comprehensive generic measure encompassing
8 attributes of health, namely, vision, hearing, speech,
walking, dexterity, emotion, cognition, and pain. Each
attribute has 5 or 6 levels of functioning, ranging from
normal function to severe dysfunction. The level at
which a subject functions with regard to each of the 8
attributes is established through questionnaires or interviews, and results are then used to determine an 8-element
health status vector.
A utility function makes it possible to assign a continuous multiple-attribute utility (MAU) to any particular health status identified. In this study, we used the
utility function described by Furlong et al,26 on the basis
of a Canadian preference study. This MAU is an estimate
of a population-based preference for a specific health
state, yielding an index in which 0 indicates dead and 1.0
indicates perfect health. Furthermore, utilities can be
calculated for each separate attribute, resulting in singleattribute utilities (SAUs). Below we report on both SAUs
and MAUs.
HUI3 data can be collected through either paper-andpencil questionnaires or direct interviews. The respondents may be the patients themselves or proxies such as
parents. In this study, the primary sources of information were the adolescents themselves.
Methods of data collection varied from one cohort to
the next. In Ontario, all data were collected in face-toface interviews with children and parents. In Bavaria
and the Netherlands, data were collected with questionnaires mailed to the children and questionnaires completed by parents. In the Netherlands, 8 children who
were expected not to be able to complete paper-andpencil questionnaires were approached for face-to-face
interviews. The children had been classified at age 5 by
a trained pediatrician as having a major handicap, such
as severe CP, mental retardation, blindness, deafness, or
combination of these conditions, leading to interference
with daily living and thus a life of dependency or institutionalization.
In each cohort, a number of severely impaired adolescents were unable to provide information. In such
cases, it was decided to substitute proxy information
obtained from parents/caregivers, if available. By using
the results of the study on method and source effects by
Verrips et al,27 these substituting scores were corrected
with a constant calculated separately for each of the
cohorts, on the basis of data for children for whom HUI3
information from both parent and child was available;
this constant constituted the mean difference between
the children’s scores derived via the main method used
for each cohort and the substituting data.
Birth weight and gestational age were recorded carefully at the time of birth10,28 and were assumed to be
internationally comparable. Because Paneth et al29
found that the diagnosis of disabling CP was much more
consistent between these cohorts than was nondisabling
CP, we used disabling CP data in our analyses.
Analyses
To test the statistical significance of HRQL differences
between cohorts, first the variable cohort was entered
into an analysis of variance as a factor. To evaluate
whether birth weight, gestational age, and CP might be
related to cohort differences, subsequently these variables were entered as covariates.
HUI3 outcomes tend to be skewed. As a result, analyses of variance might not be able to detect relevant
differences in distributions. Therefore, we performed additional logistic regression analyses with dichotomized
SAU and MAU values (1 vs ⬍1). Again, cohort was
entered into the analyses first, followed by birth weight,
gestational age, and CP. Because the standard output
from logistic regression analyses in SPSS (SPSS, Chicago,
IL) does not allow for pairwise visual comparisons between cohorts, the analyses were repeated twice, first
with Ontario as the reference category and then with
Bavaria as the reference category. Simple contrasts were
assessed. Wald index values were used to evaluate the
effect of cohort as a predictor, and odds ratios with 95%
confidence intervals not including 1 indicated significant
differences between cohorts in pairwise comparisons.
In cross-cultural comparisons of cohorts, it may be
advisable not to use raw scores but to use some transformation, correcting for possible differences in the disPEDIATRICS Volume 122, Number 3, September 2008
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557
TABLE 1 Characteristics of Survivors Until Assessment
No. of births
Survived until assessment, n (%)
Birth weight, mean ⫾ SD, g
Gestational age, mean ⫾ SD, wkd
Prenatal steroid treatment, n (%)
Ventilated, %
Ventilation time, median
(interquartile range), d
Nonventilated, received CPAP,
n (%)
Received surfactant
Hospital time, mean ⫾ SD, d
Proportion of children with
nondisabling CP, %
Proportion of children with
disabling CP, %
TABLE 2 Main HUI3 Outcomes
Ontarioa
Bavariab
Netherlandsc
397
169 (43)
834 ⫾ 126
27.0 ⫾ 2.3
76 (45)
82
32 (12–56)
203
91 (45)
888 ⫾ 101
28.5 ⫾ 2.0
Unknown
95
38 (20–57)
310
140 (45)
882 ⫾ 105
29.3 ⫾ 2.3
43 (31)
53
6 (2–11)
None
None
100 ⫾ 32
9.3
4.0
All
16 (11)
None
120 ⫾ 47
4.6
4.6
None
97 ⫾ 32
4.8
1.6
CPAP indicates continuous positive airway pressure.
a McMaster ELBW Cohort (Ontario).
b Bavarian Longitudinal Study of Children at Biological Risk (Bavaria).
c Project on Preterm and Small for Gestational Age Infants (Netherlands).
d Decimal is a true decimal; gestational age was recorded in weeks completed.
tribution of the reference samples.10 For this study, reference data were available from control groups and
normative population samples as follows: 123 sameaged, term, control children in Ontario, 294 same-aged,
term, control children in Bavaria, and a normative population sample of 1320 adolescents, 14 years of age, in
the Netherlands. No significant difference in MAU values between national reference samples could be identified. Therefore, we used raw data in the analyses. A
significance level of .05 was used in all tests.
RESULTS
Table 1 presents information on baseline characteristics.
The proportions of children surviving until assessment at
adolescence were similar in the 3 cohorts. Mean gestational age was lowest in Ontario and highest in the
Netherlands. The Bavarian ELBW children had the highest birth weight. With regard to the prevalence of both
disabling and nondisabling CP, there were clear differences between the 3 cohorts, with the lowest morbidity
rates in the Netherlands.
Table 2 describes the samples with regard to the main
HUI3 outcomes used in the study. The response rate for
the survivors in Bavaria was lower than the rates for
those in Ontario and the Netherlands. The total number
of different HUI3 health states, standardized for 100
children, differed remarkably between the cohorts, being lowest for the Netherlands and highest for Bavaria.
This indicates a greater variety of health problems in
Ontario and Bavaria than in the Netherlands.
Table 3 shows the mean MAU and SAU scores for the
3 cohorts. Statistically significant differences were found
in the MAU and the SAUs for vision, speech and cognition. Although the mean scores for Ontario and Bavaria
were quite similar, those for the Netherlands were
higher, indicating less morbidity in the Dutch ELBW
children. Although the covariates explained variance in
558
VERRIPS et al
Assessed HUI3, n (%)
Proportion with substituted
HUI3 data, %
MAU score, mean ⫾ SD
Proportion with affected
SAU/MAU scores, %
Vision
Hearing
Speech
Walking
Dexterity
Emotion
Cognition
Pain
MAU
HUI3 health states in
samples, no. per 100
children
Ontario
Bavaria
Netherlands
150 (89)
6.0
65 (71)
13.8
126 (90)
8.7
0.76 ⫾ 0.27
0.75 ⫾ 0.29
0.87 ⫾ 0.20
42
1
21
10
5
23
57
13
81
43.3
52
3
48
5
8
35
49
31
86
69.2
26
4
25
3
4
29
21
21
65
38.1
SAU and MAU values, this did not affect cohort differences, except for vision.
Table 4 presents the results of the logistic regression
analyses. Wald index values indicated statistically significant differences between cohorts in the dichotomized
SAU scores for vision, speech, cognition, and pain and in
the dichotomized MAU values. Between Ontario and the
Netherlands, there were significant odds ratios for vision, walking, cognition, and MAU, with Dutch ELBW
children being less often affected. When the covariates
were included in the model, the differences in vision and
walking disappeared. The differences in cognition and
MAU, however, were largely unaffected. Between Bavaria and the Netherlands, there were significant differences in vision, speech, cognition, and MAU, with Bavarian children scoring less favorably. These differences
were not affected when the covariates were added to the
model. Between Bavaria and Ontario, there were significant differences for speech and pain, with more-positive
scores for the ELBW children in Ontario. When the
covariates were included in the model, an additional
difference was found for vision, with the Bavarian children scoring less favorably.
DISCUSSION
Our comparison of HRQL between ELBW adolescents in
Canada, Germany, and Holland showed substantial differences. Horsman et al30 found a 0.03 MAU difference
to be clinically significant. Therefore, the differences in
mean SAU and MAU scores we found in our study
deserve serious consideration.
All 3 cohorts were geographically defined, prospective samples. Therefore, the differences in HRQL identified in this study cannot be attributed to biased sampling.
Although all children in Ontario were cared for in 1 tertiary
neonatal center, the Bavarian ELBW children were cared
for in 17 different neonatal units and those in the Netherlands in 8 centers. In the Netherlands in 1983, ELBW
children either died or were transferred to these 8 centers.
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TABLE 3 Mean SAU and MAU Scores and Statistical Significance of Differences in Mean Scores
According to Model (No Covariates Versus Covariates Included)
Score, Mean
SAU
Vision
Hearing
Speech
Walking
Dexterity
Emotion
Cognition
Pain
MAU
Statistical Significance, P
Ontario
Bavaria
Netherlands
No Covariates
Birth Weight, Gestational Age,
and Disabling CP Included
0.966
0.997
0.977
0.987
0.990
0.974
0.923
0.979
0.760
0.960
0.992
0.949
0.988
0.986
0.978
0.928
0.983
0.752
0.988
0.994
0.979
0.992
0.994
0.979
0.978
0.984
0.868
.022
Not significant
.001
Not significant
Not significant
Not significant
⬍.0001
Not significant
.001
Not significant
Not significant
.001
Not significant
Not significant
Not significant
⬍.0001
Not significant
.011
We cannot think of a logical mechanism through which
the number of neonatal units involved might have caused
the differences observed. Also, we may assume that these
differences are not attributable to different mortality rates.
In the Netherlands in the early 1980s, intensive care was
offered selectively to infants of borderline viability. This in
itself could explain the relatively positive scores for surviving Dutch ELBW children. As Table 1 shows, however, the
rates of dropout because of death were quite similar; also,
in the Netherlands, deaths in the delivery room were included in the mortality figures.
The response rate of the survivors was lower in the
Bavarian cohort. The evidence available from follow-up
studies of preterm or low birth weight children suggests
that nonresponse is more likely to occur among those
with higher morbidity, more learning difficulties, and
lower socioeconomic status,31,32 all factors that can be
expected to have a negative impact on HRQL. This
means that the differences in HRQL found in this study
cannot be explained by the different response rates. On
the contrary, a higher response rate in Bavaria might
have resulted in even larger differences.
Although the ages at HUI3 assessment were not identical in the 3 cohorts, the respondents were all in their
early teens. The Dutch reference study included children
between 12 and 15 years of age. This was a random sample
of the normal population, in which age was not related to
HUI3 scores. However, some researchers currently hypothesize that, as preterm and/or ELBW children grow older,
more problems will emerge among them.1 This was not
proven to be the case in the Ontario study, in which few, if
any, differences were observed in the overall transition to
adulthood.33 Therefore, the age at assessment cannot explain the differences between the cohorts identified in this
study. The Dutch ELBW children were older than the
Bavarian children but seemed to have a higher overall
HRQL. Also, no differences in HRQL scores were observed
between the ELBW and normal birth weight groups in the
Ontario cohort at young adulthood.21
We cautiously conclude that the differences in HRQL
outcomes at adolescence between the 3 cohorts are real.
These differences remained significant when CP (a gross
indicator of a dysfunctional central nervous system),
birth weight, and gestational age were taken into account statistically. The key attribute explaining these
differences seems to be cognition.
We propose 3 hypotheses of how these differences
might be explained. Firstly, differences in treatment regimens may be relevant. Such differences between the 3
cohorts in this study have not been investigated systematically. However, if it is true that the Dutch regimen in
the early 1980s was at the extreme end of the “wait until
certain” approach, compared with the “statistical prognostic strategy,”17 then the Bavarian and Ontario regi-
TABLE 4 Odds Ratios of Dichotomized SAU and MAU Values (1 vs <1) According to Model (No Covariates Versus Covariates Included, Non
Significant Odds Ratios Deleted)
Odds Ratio (95% Confidence Interval)
No Covariates
Visiona.b
Speecha,b
Walking
Cognitiona,b
Paina,b
MAUb
a Wald
b Wald
Birth Weight, Gestational Age, and Disabling CP Included
Ontario vs
Netherlands
Bavaria vs
Netherlands
Bavaria vs
Ontario
2.04 (1.22–3.41)
3.09 (1.65–5.79)
2.79 (1.48–5.26)
3.50 (1.87–6.55)
3.39 (1.09–10.49)
5.17 (3.01–8.86)
3.73 (1.95–7.14)
Ontario vs
Netherlands
Bavaria vs
Netherlands
Bavaria vs
Ontario
3.02 (1.59–5.73)
2.72 (1.40–5.26)
1.89 (1.01–3.50)
4.50 (2.25–8.99)
5.18 (2.84–9.44)
3.93 (1.99–7.75)
2.06 (1.13–3.75)
3.52 (1.56–7.94)
3.06 (1.50–6.25)
2.34 (1.35–3.34)
3.34 (151–7.38)
3.18 (1.50–6.76)
Index significant (␣ ⫽ .05) in model with no covariates.
Index significant (␣ ⫽ .05) in model with birth weight, gestational age, and disabling CP included.
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559
mens probably can be characterized as more intensive
(for example, favoring cesarean section over postponement of delivery or favoring the greater use of mechanically assisted ventilation). Indeed, the Bavarian and Canadian studies used highly intensive treatment, with
95% and 82% of ELBW children, respectively, receiving
ventilation, and intensity of care was adversely related to
outcomes.13 Such differences in treatment may affect the
developing nervous system, thereby likely leading to
different rates of cognitive problems.18 Also, the fact that
the Dutch children had a greater mean gestational age
(although it was not statistically significant) may add to
the picture of relevant treatment differences explaining
our results. This is an important area for additional research, although such research may be difficult, because
randomized, controlled trials for offering neonatal intensive care are ethically impossible and also because longterm effects have yet to be studied.
Secondly, it has been shown that social and cultural
factors can compensate for perinatal disadvantages; as
the children grow older, the impact of biological and
perinatal risk factors diminishes and other factors, such
as the socioeconomic status and educational level of the
parents, seem to have a greater influence on the cognitive performance of low birth weight and preterm children.3,34,35 Possibly such social and cultural factors are
part of the explanation for the differences found in this
study. In principle, it is possible to explore whether this
is the case by including such socioeconomic parameters
in the analyses, carefully defining them to ensure equivalence between cohorts. Also, wider social policies and
cultural contexts may have an impact on the HRQL and
well-being of children. A recent UNICEF report36 on the
well-being of children in 21 rich countries found that the
Netherlands ranks first in the overall educational, social,
and health-related well-being of children, whereas Germany and Canada rank 11th and 12th, respectively. The
favorable general conditions of care for children in the
Netherlands may be reflected in the health status of very
preterm children, just as socioeconomic status has been
found to affect extremely preterm children in the same
way as term children.35
Thirdly, there is the possibility that attitudes toward
disabilities and handicaps vary from one country to another, expressing themselves, for example, in different
criteria for entry to special education and other resources
and facilities for those with special needs.21 Such differences might mean that people with disabilities face different task requirements and different experiences of
failure. If so, this might mean that, even if functional
restrictions were broadly similar in objective terms, subjective awareness of them would be different. This could
be evaluated in analyses in which objective and carefully
defined indicators of restrictions were related to patient
self-reports.
Because of the lack of internationally comparable
data, we were unable to explore the influence of socioeconomic and cultural differences on HRQL. These influences should be studied in more detail, because they
may indicate important ways to improve the HRQL of
these children. These approaches could include medical
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VERRIPS et al
and nonmedical interventions that enable neonatologists and other professionals to optimize the care of
ELBW infants, especially after the period of neonatal
intensive care.
HRQL differences found in our study, especially in the
cognitive domain, may affect the functioning of ELBW
children later in life differently in different countries.
Our study clearly showed that it is important to evaluate
the impact of ELBW on health and HRQL in different
countries, because findings from one country may not be
easily generalized to another.
ACKNOWLEDGMENTS
This study was supported by grant HS-08385 from the
Agency for Health Care Policy and Research. The German part of the study was supported by grants JUG 14
(Bundesministerium fur Bildung und Forschung), FKZ
01 EP 9504 (Ministry of Education and Science), and
FKZ SCHN 315/151 (German Research Council).
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PEDIATRICS Volume 122, Number 3, September 2008
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561
Health-Related Quality of Life for Extremely Low Birth Weight Adolescents in
Canada, Germany, and the Netherlands
Erik Verrips, Ton Vogels, Saroj Saigal, Dieter Wolke, Renate Meyer, Lorraine Hoult
and S. Pauline Verloove-Vanhorick
Pediatrics 2008;122;556-561
DOI: 10.1542/peds.2007-1043
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