PEDIATRICS Vol. 122 No. 3 September 2008, pp. 556-561 (doi:10.1542/peds.2007-1043)
ARTICLE |
Health-Related Quality of Life for Extremely Low Birth Weight Adolescents in Canada, Germany, and the Netherlands
a Applied Scientific Research (TNO) Prevention and Health, Leiden, Netherlands
b Academic Centre Dentistry Amsterdam, Amsterdam, Netherlands
c Department of Pediatrics, McMaster University, Hamilton, Canada
d Department of Developmental Psychology, University of Warwick, Coventry, United Kingdom
e University of Munich Children's Hospital, Munich, Germany
f Department of Pediatrics, Leiden University Medical Center, Leiden, Netherlands
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ABSTRACT |
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 TOP ABSTRACT METHODSRESULTSDISCUSSIONREFERENCES |
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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.
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
Follow-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.
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.
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METHODS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONREFERENCES |
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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
1000 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 1000 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 single-attribute utilities (SAUs). Below we report on both SAUs and MAUs.
HUI3 data can be collected through either paper-and-pencil 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-to-face 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-and-pencil 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 distribution of the reference samples.10 For this study, reference data were available from control groups and normative population samples as follows: 123 same-aged, 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.
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RESULTS |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONREFERENCES |
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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.
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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.
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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 SAU and MAU values, this did not affect cohort differences, except for vision.
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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.
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DISCUSSION |
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TOPABSTRACTMETHODSRESULTSDISCUSSIONREFERENCES |
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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. 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 regimens 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 long-term 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 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.
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ACKNOWLEDGMENTS |
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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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FOOTNOTES |
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Accepted Dec 14, 2007.
Address correspondence to Erik Verrips, PhD, TNO Prevention and Health, PO Box 2215, 2301 CE Leiden, Netherlands. E-mail: erik.verrips{at}tno.nl
The authors have indicated they have no financial relationships relevant to this article to disclose.
| What's Known on This Subject Health-related quality of life in adolescence may be affected by ELBW, compared with normal birth weight.
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| What This Study Adds 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.
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PEDIATRICS (ISSN 1098-4275). ©2008 by the American Academy of Pediatrics
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