1 Section for Medical Statistics, Department of Public Health and Primary Health Care, University of Bergen, Norway.
2 Medical Birth Registry of Norway, University of Bergen, Norway.
Correspondence: Kari Klungsøyr Melve, Section for Medical Statistics, Armauer Hansens Building, N-5021 Bergen, Norway. E-mail: Kari.Melve{at}isf.uib.no
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Abstract |
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Methods Births in the Norwegian Medical Birth Registry from 1967 to 1998 were linked to their mothers through their national identification numbers. The study population was 546 688 mothers with at least two singletons weighing 500 g at birth. Weight-specific perinatal mortality for second-born siblings in families with first-born siblings in either the highest or the lowest birthweight quartile was analysed. Maternal education and cohabitation status were used as measures of socioeconomic level.
Results For all 500-g categories below 3500 g, mortality rates were significantly higher among second-born infants with an older sibling in the highest rather than the lowest weight quartile. This pattern was the same across three educational levels. The exclusion of preterm births did not change the effect pattern. A comparison of perinatal mortality among second siblings in terms of relative birthweight (z-scores) showed a reversal of the relative risks, although these were only significantly different from unity for the smallest infants.
Conclusion The crossover in weight-specific perinatal mortality for second siblings by weight of first sibling is largely independent of socioeconomic level, and is not weakened by the decreasing perinatal mortality rates in the population over time. Family data should be taken into consideration when evaluating the risk of adverse pregnancy outcome relating to weight.
Accepted 19 February 2003
Birthweight is closely associated with perinatal mortality and morbidity, and also with disease later in life.13 Categorical risk indicators based on birthweight are therefore much used in perinatal clinics and research, for instance low birthweight (LBW, weight <2500 g). The relations between perinatal outcome and these indicators do not, however, reflect the considerable heterogeneity within the categories.
Due to different birthweight distributions in different populations, with crossover in mortality rates by weight, Wilcox and Russell have called for population-specific standards for birthweight.4 This suggestion has been criticized by others, for instance Carlson and Hoem in a study on the Czech Republic.5 The Czech Republic has an ethnically homogenous population and uniform health care for all social classes. The authors suggest that in this situation, different birthweight distributions and associated mortality rates are results of underlying differences in lifestyle and social conditions, rather than of biological differences. This view is shared by others.6,7
When studying sibships, we get a picture of both biological (e.g. genetic) and environmental influences on birthweight. There is an acknowledged correlation between the birthweights of siblings, and this has consequences for weight-specific perinatal mortality.815 In a study on data from the Medical Birth Registry of Norway (MBRN) from 1967 to 1984, Skjaerven and colleagues showed that weight-specific perinatal mortality for second-born infants was strongly dependent on the birthweight of first-born siblings.12 This study did not, however, evaluate socioeconomic levels. Moreover, the perinatal mortality rate in Norway has decreased steadily during the years since this study was done, and the associations noted in the past may have changed.
The aim of the present study was thus to analyse weight-specific perinatal mortality among second-born siblings relative to birthweight of the first-born siblings, and to evaluate whether the associations were dependent on socioeconomic levels.
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Materials and Methods |
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The national identification number was used to link births to their mothers, providing sibship files with the mother as the observation unit. The present study was based on 546 688 mothers with at least two singleton births weighing 500 g at birth. We analysed data on first- and second-born infants only. The families were categorized into four groups (A, B, C, and D) based on the birthweight quartile to which the first-born infant belonged (Figure 1
). Second-born infants with the older sibling in the lowest (group A) and the highest (group D) weight quartiles were compared, with group A families as reference.
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Gestational age is closely related to both birthweight and perinatal mortality. In the MBRN, gestational age is based on reported menstrual dates, which will be uncertain for a proportion of mothers. When adjusting birthweight for gestational age, data were screened for birthweightgestational age consistency using a method described elsewhere.17 We also repeated some of the main analyses on term infants only (37 completed weeks).
In the present study, mothers attained education and cohabitation status were used as proxy variables for socioeconomic level. Attained education was the mothers highest level of education, measured in completed years and type of school, as registered in 1998 in the Register of Level of Education. For some mothers this level might have been achieved after childbirth. Education was categorized in three groups: 10 years, 1114 years, and
14 years, according to official standards.18 For approximately 3% of the total number of mothers in groups A and D (n = 274 112) education data were missing.
As possible confounding variables for the association between birthweight of the first-born siblings and second siblings weight-specific mortality, we evaluated maternal age, interval between births, and time period, as well as maternal education and cohabitation status. Time period was analysed by grouping year of first birth into 5-year categories from 1967 to 1996. We also compared results from the time period 19671984, which was the period covered by Skjaerven et al.,12 with results from 1985 to 1998.
Statistics
For categorical variables, we calculated crude odds ratios (OR) (tested by 2 tests), and we used logistic regression analyses to evaluate and adjust for confounding. In these models, the independent variables were treated as categorical factors, as shown in the footnotes to the Tables. To analyse mortality by relative weight, we used birthweight z-scores for second-born siblings in group A and group D families (i.e. we transformed the observed birthweight to a standard normal deviate score within each family group).
Analyses were done using SPSS v. 11.19
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Results |
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Table 1 shows second siblings mean birthweights with standard deviations in family groups A and D by level of maternal education in 19671984 and in 19851998. Second siblings mean birthweights increased with maternal education in both family groups, whereas the standard deviations decreased. Mean birthweights were >600 g higher for group D than for group A siblings for all levels of education and for both time periods. Total perinatal mortality decreased as the mean birthweights increased, and was significantly lower for second siblings in group D than group A. The proportion of mothers at the lowest educational level was higher in group A than group D, as was the proportion of single mothers (4.6% in family group A and 2.7% in family group D).
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We used logistic regression to adjust the crude OR for maternal education, cohabitation status, maternal age, inter-birth interval, and time period. This did not significantly change the results (Table 2). None of the potential confounding variables changed the crude weight-specific OR by more than 15% when evaluated singly. We further adjusted for gestational age in order to look at the growth component of the birthweight relation. The main effect pattern remained the same.
Socioeconomic level: stratifying according to mothers education
To study more closely the association between the described sibling relations and socioeconomic levels we stratified according to maternal education (three strata), and again analysed weightspecific perinatal mortality and odds of perinatal death for second siblings with large, relative to those with small, older siblings (Table 3). Adjustment for maternal age, inter-birth interval, and time period had little effect on the OR. For all levels of education, perinatal mortality was significantly higher among second-born infants weighing between 2000 and 3500 g when the older sibling was large rather than small. It is noteworthy that the OR of perinatal death for second siblings weighing 25002999 g increased from just over 3 to more than 14 in step with the rise from the lowest to the highest level of maternal education. At the highest level, the perinatal mortality rate for second siblings in group A was only 4.5 per 1000, whereas in group D it was 61.8 per 1000. The corresponding rates at the lowest educational level were 7.3 and 22.7 per 1000. In a multiple logistic regression analysis the interaction between weight of older sibling and level of maternal education was statistically significant in this weight category (Wald test, P = 0.004). There were around nine times more second siblings from group A than from group D in this birthweight category (16.0 versus 1.7% for the high educational level, 19.1 versus 2.0% for the intermediate level, and 22.4 versus 2.8% for the low educational level).
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Discussion |
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The association between birthweight and mortality is one of the most studied topics within perinatal epidemiology. The weight-specific mortality curve has an inverse J-pattern, and is highest for the smallest infants.2 High-risk infants are therefore frequently defined in terms of birthweight determinants, such as LBW. The problems of different birthweight distributions commonly found when comparing populations are, however, often ignored.1,4,2123 When comparing mortality among LBW infants from populations with different proportions of small infants, mortality is usually lowest in the population where LBW is most frequent (the LBW paradox). Several researchers explain this paradox by underlying differences in lifestyle and social conditions rather than in terms of biological differences in the birthweight distributions.57
In the present study, we focus on a variant of the LBW paradox. We describe a situation with crossover in weight-specific mortality curves where biological factors undoubtedly contribute to the different birthweight distributions. The acknowledged positive correlation between siblings birthweights, partly explained by genetic influence, has implications for weight-specific perinatal mortality risk.813 As previously reported in the study by Skjaerven et al.,12 there are sizeable differences in perinatal mortality among second-born infants in one and the same birthweight category depending on the birthweight of their older sibling.
These findings were consistent at all three levels of maternal education. However, the OR of perinatal death for second siblings weighing 25002999 g at birth were not homogenous across the educational levels, but increased significantly with increasing level. In this weight category, infants with a small older sibling probably do not deviate too much from their expected birthweight. It might be that these infants profit significantly from their linkage to a high socioeconomic level, as suggested by the low perinatal mortality rate at this level. Variation in birthweight is smaller at the highest educational level than at the other levels (Table 1), perhaps due to fewer environmental and random influences. Deviation from expected fetal growth may thus indicate a more serious situation in this group. The result is high perinatal mortality among second siblings weighing 25002999 g with an older sibling in the highest weight quartile and whose mothers had a high level of education.
On a group level, there are more risk factors associated with families whose first-born infants are in the lowest weight quartile than with those whose first-born infants are in the highest quartile (Table 1). The reason why we find lower weight-specific mortality among second infants in these families is that mortality is compared in terms of absolute birthweight. A given birthweight value has different locations in the two second-born infants weight distributions, and thus also on the corresponding weight-specific mortality curves.23 When we instead compare perinatal mortality among second siblings according to relative weight, the difference in risk is small and reversed, with highest weight-specific mortality in families whose first-born infant was in the lowest birthweight quartile.
Some factors in this study need specific discussion. Firstly, perinatal mortality is a compound measure of stillbirth and early neonatal mortality. Although they display many similar features, these measures also differ in several ways; firstly in terms of clinical consequences, but also in terms of certain causes and risk factors.24 For some research questions, perinatal mortality may be a less relevant outcome than either neonatal mortality or stillbirth rate. However, the distinction between stillbirth and neonatal death is not always straightforward, and differences may occur in registration of the smallest infants as either stillborn or live-born when vivid signs of movement or respiration are observed before death.25 The policy of elective delivery for the most preterm fetuses will also influence mortality rates. We therefore chose to use perinatal mortality as the outcome in our analyses.26 However, we repeated all analyses using neonatal mortality (first month) and stillbirth rate as separate outcomes. The main patterns of results were similar for both, and results are available on request.
The risk of perinatal death for second siblings is associated with the outcome for their older siblings, and is higher in families with a previous loss.8 We repeated the analyses after excluding families with a perinatal loss in the first birth, and the results were close to those described.
Attained education and cohabitation status are used as proxy variables for socioeconomic level. Socioeconomic level is a construct used to define social inequality. It is usually measured in terms of income, cohabitation status, occupation, and/or educational attainment.5,2729 Education is the dimension of socioeconomic level that is most strongly and consistently associated with health among women and their children.2930 In recent years there has been a debate about epidemiological studies of individuals versus studies of populations in the attempt to understand health problems that go beyond the proximate, individual-level risk factors.3133 There has been a call for theories that integrate genes (or other biological variables) within their broader behavioural, cultural and social contexts (ref. 33, p. 1030). Our conceptual model is based on the assumption of existing causal pathways, where society-level determinants may be viewed as antecedents to individual-level exposures and behaviours.29 This does not necessarily imply a simple linear chain of causality. Thus, our main results are displayed in Table 3, showing evidence of biological influence on fetal growth within each stratum of socioeconomic level.
The influence of socioeconomic level on perinatal outcome may vary between countries and may have a higher impact in the Czech Republic5 and the US30 than in the Scandinavian countries, at least where population attributable fractions are concerned. However, studies have shown that socioeconomic level has a significant impact on perinatal outcome also in Scandinavia,34,35 as is evident in our results (Tables 1 and 3). The associations found with the fathers educational level were slightly weaker, but within approximately the same range.
Similar to the Czech Republic, Norway has an ethnically homogenous population, whereby proportions of immigrants from countries outside Western Europe and North America were 0.1% in 1970, 0.5% in 1980, 1.8% in 1990, and 2.7% in 1998 (Statistics Norway). An increase in low birthweight deliveries after 1980 in Oslo and its adjacent regions (where the majority of immigrants settle) was not found to be due to the increased proportion of infants born to immigrant women.36 Our results suggest that biological factors may cause variation in the birthweight distribution even in an ethnically homogenous population. Birthweight variation is also found among healthy individuals, reflected in this study by similar survival rates for average-sized term second siblings (z-scores from 0.4 to 0.4) in the two family groups (Figure 3), for whom the absolute mean birthweight differed by more than 600 g.
In conclusion, we find that familial factors are of importance for birthweight distributions, independent of socioeconomic level. Socioeconomic level plays a part in explaining the LBW paradox, but does not tell us everything. Improving social conditions (or at least educational level) will reduce perinatal mortality, but will not remove the biological link in siblings birthweights. For clinicians working in antenatal care, this underlines the importance of taking note of a womans previous birth(s) as well as of her socioeconomic level. Information about siblings birthweight(s) will be of value when evaluating fetal growth in an ongoing pregnancy. For research planning, follow-up studies linking birthweight to perinatal outcome should take family data on birthweight and gestation into account. Risk categories defined solely on the basis of absolute birthweight, with common cut-off values for all individuals (e.g. <2500 g), should be avoided.
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Acknowledgments |
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References |
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