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Keywords:

  • Preterm infant;
  • registries;
  • reproduction;
  • small-for-gestational-age infant;
  • women

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgements
  9. References

Objective  To estimate the intergenerational effects of preterm birth and reduced intrauterine growth.

Design  Population-based cohort study.

Settings  Mother–first-born offspring pairs recorded in the Swedish Medical Birth Registry.

Population  Children born before 2001 to 38 720 women born in 1973–75.

Methods  The relationships between the mother's and the child's birth characteristics were estimated using logistic regression analysis. Adjustments were made for smoking habits, body mass index (BMI), and current and childhood socio-economic conditions. Analyses were performed on all mother–offspring pairs and on the pairs for which information on neither of the included background variables was missing (n= 24 520).

Main outcome measures  Preterm birth (<37 weeks of gestation) and small for gestational age (SGA) (<−2 SD of the Swedish standard).

Results  Mothers who themselves had been born preterm were not significantly more likely to deliver their own children preterm, compared with those who had been born at term (adjusted OR 1.24, 95% CI 0.95–1.62). Also, preterm birth in the mothers did not influence the occurrence of SGA in the children. However, the odds ratio for giving birth to SGA and preterm children, respectively, was higher among SGA mothers (OR 2.68, 95% CI 2.11–3.41 and OR 1.30, 95% CI 1.05–1.61). Mothers whose intrauterine growth was moderately reduced but who did not meet the criterion of being born SGA were also at higher risk of giving birth to both preterm and SGA children, respectively.

Conclusions  The present study showed evidence of intergenerational effects of reduced intrauterine growth even when socio-economic factors as well as BMI and smoking were adjusted for. There was, however, no consistent intergenerational effect of preterm birth.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgements
  9. References

Reduced intrauterine growth and preterm birth seem to be related to subsequent adverse outcomes, such as altered physical health in adult life,1 poorer school performances,2–5 and, to some extent, impaired reproduction in women.2,5–7 Researchers have also speculated about a connection between reduced intrauterine growth as well as preterm birth in the mother and/or father and different adverse pregnancy outcomes in the offspring, such as increased infant mortality,8,9 and preterm birth as well as reduced intrauterine growth.10–20 Previous research has shown an intergenerational effect in birthweight,10–13,17,18 and in some of the studies, adjustments were made for gestational age. Other studies have used a slightly different approach when evaluating the intergenerational effect of birthweight for gestational age by dichotomising the birthweight for gestational age into small for gestational age (SGA), typically defined by external standards, and appropriate weight for gestational age (AGA).14–16,20 These studies also suggest that an effect might be present, but larger studies are needed to confirm these findings. The intergenerational effect of preterm birth has also been investigated and although these studies taken together provide evidence of an intergenerational effect, the conclusions drawn in these studies are not always in agreement.15–17,19,20 In a few studies, preterm birth in the mother has been related to SGA in the offspring and vice versa,15,16,20 but the relationships were not well established.

Socio-economic factors such as parental education, maternal age when giving birth, and marital status play a part in determining the occurrence of preterm birth and SGA in the offspring2,8,21,22 as do the mother's body mass index (BMI) and smoking habits. Preterm birth and SGA may have consequences related to the socio-economic situation as well as the BMI, and smoking habits in adult life.2,3,5,11,15,16 There is growing evidence that the intergenerational effects of preterm birth and SGA (as well as birthweight and gestational length) persist even after adjustments are made for current socio-economic status of the mother and/or father.10–13,16,19,23,24 Previous research has also shown an association between maternal social and environmental factors during the childhood of the mother and birthweight of her offspring.25 Therefore, it is of value to try to account not only for the mother's current socio-economic factors but also for the socio-economic situation during her own childhood, when investigating the intergenerational effects of preterm birth and SGA. In addition, it is important to account for the mother's BMI and smoking habits when investigating the intergenerational effects, as these variables otherwise could confound the results. Studies have indicated that the mother's smoking habits or weight/BMI during pregnancy, or her socio-economic situation during childhood, are not likely to explain the intergenerational effects found.12,14–16,23,24 Although some previous studies have been able to simultaneously take account of the above-mentioned variables,23,24 the number of observations in these studies is relatively small, which affects the power of the study particularly when studying the more pathological groups (i.e. preterm and SGA).

Swedish population-based registries offer an opportunity to study the intergenerational effects of preterm birth and SGA in a large sample. Through the registries used, it is possible to get access to socio-economic characteristics of the mothers and the mothers’ parents (also referred to as grandparents), as well as information on the mothers’ smoking habits and BMI at the time of pregnancy. Thus, the primary aim of this study was to estimate the intergenerational effects of preterm birth and reduced intrauterine growth (i.e. SGA) and to evaluate the impact of current and childhood socio-economic factors, as well as the mothers’ BMI and smoking habits during pregnancy, on these effects.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgements
  9. References

Since 1973, all births in Sweden are registered in the Swedish Medical Birth Registry (MBR),26 which contains information on birth outcomes as well as certain maternal characteristics such as previous reproductive history, and family situation and age when giving birth. In the 1980s and 1990s, additional information was given, for example, the smoking habits and weight of the mothers.27 The births and deaths reported to the MBR are validated annually against the Total Population Register (TPR)28 through individual record linkage. The TPR also contains information on deaths, marital status, migration, and country of origin for Swedish residents born abroad. Other registries used were the Causes of Death Register29 in which information on all deceased persons is recorded, and the Multi-generation Register,30 which makes it possible to identify the parents and grandparents of the children registered in the MBR and the TPR. Information on educational levels was included by using the Education Register31 and the 1970 Population and Housing Census.32

A total of 155 494 female births were registered in Sweden during the years 1973, 1974, and 1975 according to both the MBR and the TPR. Of the women who were alive and still living in Sweden at 13 years of age (n= 150 425), 1029 were excluded due to missing values on birthweight and/or gestational length, as were 126 women with extremely high birthweights compared with the length of gestation (the procedure of exclusion is explained in a previous study2). The remaining 149 270 women were then individually linked to the maternal personal identification numbers for births occurring in the MBR before the year of 2001 (the first birth occurred in 1987). Thus, at the time of the study, the maximum maternal age was 27 years. A total of 40 152 mother–first-born offspring pairs were identified, of which 262 pairs were excluded due to missing values on the child's birthweight and/or gestational length and an additional five due to the children having extremely high birthweights compared with the length of their gestation. Missing values on the first-born child's birthweight and gestational age were more common among teenage mothers and among mothers with lower educational levels. Furthermore, mother–offspring pairs where either the mother or the child was the result of a multiple birth (n= 548 and 356, respectively) were excluded, as were pairs for whom the grandmother and/or grandfather could not be identified in the registries (n= 273). Thus, 38 720 mother–offspring pairs were available for analysis.

Preterm birth was defined as birth before 37 completed weeks of gestation; SGA was defined according to the Swedish standard (i.e. as a birthweight at least 2 SD below the mean weight for the gestational length).33 The impact of the mother's gestational length, as well as her birthweight for gestational length, on the occurrence of preterm birth and SGA in the child was also evaluated in more detail. In these analyses, the gestational length of the mother was split into four categories: ≤31, 32–36, 37–41, and ≥42 weeks of gestation. The mothers’ birthweight for gestational length was expressed in standardised SDs and then split into six categories: <−2, −2 to −1.01, −1 to −0.01, 0 to +1, +1.01 to +2, and >+2 SDs. The standardised SDs were computed by using the formula presented by Marsal et al.33 A similar categorisation of the birthweight for gestational age was also performed on the children.

Data on the mothers’ age, smoking habits, weight, height, and cohabitation with the infant's father were collected by the midwives at the first antenatal visit, and then recorded in the MBR. Smoking was coded as ‘yes’ or ‘no’, as was the cohabitation variable. The mother's age when giving birth was split into three categories: ≤19, 20–24, and ≥25 years. The weight and height of the mothers were used to calculate the BMI, which was divided into four categories: <20, 20–24.9, 25–29.9, and ≥30. Information on the educational levels and marital status of the mothers at 25 years of age as well as information on the socio-economic situations of the mothers’ parents in 1970 (i.e. close to the time of the birth of the mothers) were collected and included in the analyses. The educational levels were coded according to the Swedish educational system: elementary school (9 or 10 years of education), high school (11–13 years), and graduate and postgraduate education (14 years or more). The marital status of the mothers was coded as ‘married’ or ‘unmarried’, and the marital status of the grandmothers as ‘married’, ‘unmarried’, and ‘divorced or widowed’. The grandmothers’ parity at the time of giving birth to the mothers in the study was split into two categories: no previous children and one or more previous children; and their age was split into four categories: ≤19, 20–26, 27–33, and ≥34 years. Information on the grandparents’ country of origin was coded to indicate if at least one of the grandparents had been born outside the Nordic countries or if both grandparents had been born in the Nordic countries.

Information on the mothers’ smoking habits, cohabitation status, and BMI was missing in 5.4, 6.0, and 16.1% of the 38 720 mothers, respectively. As data on maternal weight were not available in the MBR during the years 1990–91,27 missing values on BMI were more common among teenage mothers. The same held true for the cohabitation variable, even though information was recorded in the MBR during the whole study period. Missing values on smoking habits were more common among mothers aged 25 years or more when giving birth to their first child. Information on the educational level of the grandmothers was missing in 14.8% of the mother–child pairs and the corresponding percentage among the grandfathers was 8.1. It has previously been shown that the missing category on these variables constitutes a greater proportion of individuals who were young when giving birth as well as individuals who immigrated later than 1970 or were of non-Nordic origin.2,34 Missing values on other socio-economic variables of the mother and her parents were found in less than 1% of the population, for each variable.

Due to the relatively high percentages of missing values on the mothers’ smoking habits, cohabitation status, and BMI, as well as the grandparents’ educational levels, analyses were performed both on the original study population (n= 38 720) and on the mother–offspring pairs for which information on neither of the included variables was missing (n= 24 520).

Statistical analyses

The relationships between the mothers’ birth characteristics (i.e. preterm birth and SGA) and socio-economic factors as well as BMI and smoking habits in adult life (see 1Table 1) were estimated using χ2 tests. We also performed multiple logistic regression analyses in which the above-mentioned relations were adjusted for socio-economic characteristics of the mother's parents (i.e. the grandparents’ educational levels, grandmother's marital status, parity, and age when giving birth to the mothers of study, as well as the grandparents’ country of origin). In these additional analyses (see first paragraph of Results), the following categories of the dependent variables were treated as reference categories: ‘age <25 years’, ‘not smoking’, ‘living with the child's father’, ‘BMI ≥20’, ‘less than 14 years of education’, and ‘not married’. Multiple logistic regression analysis was also used when estimating the effects of maternal smoking habits, cohabitation status, age, BMI, as well as the effects of the socio-economic characteristics of the mother's parents on preterm birth and SGA in the offspring. The odds ratios were adjusted for all variables included in the models, and the variables were treated as categorical.

Table 1.  Preterm birth and SGA among the mothers in relation to subsequent socio-economic factors as well as BMI and smoking habits*
 Mother born preterm**Mother born SGA**
 Yes (n= 807)No (n= 23 713)P***Yes (n= 1283)No (n= 23 237)P***
  • *

    n= 24 520.

  • **

    Values are presented as percentages.

  • ***

    P-value for χ2 tests.

  • The variables were measured at the time of pregnancy.

  • The variables were measured when the mothers were aged 25 years.

Age (years) 0.116 0.842
≤196.86.2 6.66.2 
20–2459.156.1 56.056.2 
≥2534.137.6 37.337.5 
Smoking habits 0.111 0.001
Not smoking79.381.5 78.181.6 
Smoking20.718.5 21.918.4 
Cohabitation status 0.001 0.124
Living with the child's father88.792.0 90.791.9 
Other family situation11.38.0 9.38.1 
BMI 0.831 <0.001
<2013.614.0 18.513.7 
20–24.954.855.9 54.455.9 
25–29.923.021.8 20.321.9 
≥308.68.4 6.98.4 
Educational level (years) <0.001 <0.001
9–1018.314.7 17.114.7 
11–1368.567.5 69.667.4 
≥1413.117.8 13.117.9 
Marital status 0.693 0.015
Married22.322.9 20.123.0 
Not married77.777.1 79.977.0 

In addition, logistic regression analysis was used to estimate the intergenerational effects of preterm birth and SGA. Analyses were performed both on the original study population (n= 38 720) and on the mother–offspring pairs for which information on neither of the included variables was missing (n= 24 520). Crude odds ratios and 95% confidence intervals were calculated for each analysis, and, for the 24 520 mother–offspring pairs with complete information on the background variables, adjusted odds ratios were also calculated. Adjustments were made for the mothers’ smoking habits, cohabitation status, age, and BMI, as well as for the socio-economic characteristics of the mothers’ parents. Since the mothers’ educational level and marital status were measured at 25 years of age, we performed subgroup analyses where only the mothers aged ≥25 years when giving birth were included, and we then evaluated the effect of these two additional variables on the investigated intergenerational effects.

The impact of the mother's gestational length and the mother's birthweight for gestational length (expressed in SDs) on the occurrence of preterm birth and SGA in the child was also evaluated in more detail. These models were compared with the previously described models (i.e. in which the mother's gestational length and birthweight for gestational length were dichotomised) using the −2ln(likelihood) of the models. In this way, it was possible to evaluate if the more detailed categorisations of the mother's gestational length and birthweight for gestational length, respectively, added more information in explaining the occurrence of preterm birth and SGA in the offspring. In addition, the effect of the mother's birthweight for gestational length on reduced intrauterine growth in the children was also evaluated in more detail by performing a series of three multiple logistic regression analyses. In the first analysis, all mother–offspring pairs were included; in the next analysis, we excluded children who were SGA (i.e. whose birthweights for gestational age were <−2 SD) and examined the children whose birthweights for gestational length were between −2 and −1.01 SD. Finally, we excluded all children below −1 SD and evaluated the effect of the mother's birthweight for gestational length on the children being born −1 to −0.01 SD.

In order to evaluate the final models, two-way interactions (also referred to as effect modifiers) between the mothers’ birth characteristics and the other background variables of the mothers were created and included in the models. The interaction terms were determined by means of forward stepwise regression, and the significance level of the interaction terms was set to P≤ 0.01.

This study was approved by the Human Research Ethics Committee, Faculty of Health Sciences, Linköping University.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgements
  9. References

Socio-economic factors as well as BMI and smoking habits in relation to birth characteristics of the mothers and first-born children

Of the mothers for whom we had complete information on all socio-economic factors as well as BMI and smoking habits, 3.3% were born preterm and 5.2% were SGA. The corresponding percentages among all eligible mothers were 3.7 and 5.5, respectively. Mothers who were born SGA smoked at the time of their own pregnancy to a greater extent than mothers who were AGA, and also had lower pre-pregnancy BMI (Table 1). Both SGA mothers and mothers born preterm displayed lower educational levels at the age of 25 compared with mothers born AGA or at term, respectively. In addition, mothers who were born preterm were less likely to be living with the child's father at the time of pregnancy, while mothers born SGA seemed to be less likely to have married at the age of 25. All associations in Table 1 remained significant when adjusted for socio-economic characteristics of the mother's parents by means of multiple logistic regression analysis (data not shown).

Of the 38 720 children in the whole cohort, 6.4% were born preterm and 2.8% were SGA. The corresponding percentages among the mother–offspring pairs with complete information on all background variables were 6.2 and 2.6. In 2Table 2, the effects of the socio-economic characteristics of the mothers’ parents and mothers, respectively, as well as the effects of the mothers’ BMI and smoking habits during early pregnancy, on the occurrence of preterm birth as well as SGA in the children are presented. Preterm birth in the child was associated with the mother's age when giving birth, and was also associated with the grandmother's educational level and age at the time of giving birth to the mothers in the study. The mother's pre-pregnancy BMI was also found to be related to preterm birth in the offspring, and preterm birth was more common among mothers with BMI ≥25. There was also some evidence that underweight mothers (i.e. BMI <20) were more likely to give birth to SGA children. In addition, mothers who had lower educational levels at the age of 25 were more likely to give birth to SGA children, whereas the mother's marital status at 25 did not affect the outcome of her pregnancy (data not shown).

Table 2.  Current and childhood socio-economic characteristics, as well as BMI and smoking habits of the mothers in relation to preterm birth and SGA in the first-born child.*Multiple logistic regression
 Child born pretermChild born SGA
 n**OR***95% CIPOR***95% CIP
  • *

    n= 24 520.

  • **

    Total number of observations in the category.

  • ***

    Odds ratio, adjusted for all variables, is presented in the table.

  • The mother's variables were measured at the time of pregnancy.

  • The grandparent's variables were measured at the time of giving birth to the mothers.

Mother's age (years) 0.048 0.386
≤1915301.230.99–1.53 1.050.77–1.43 
20–2413 789reference reference 
≥2592011.111.00–1.25 1.130.95–1.34 
Mother's smoking habits 0.534 <0.001
Not smoking19 972reference reference 
Smoking45480.960.83–1.10 2.111.77–2.52 
Mother's cohabitation status 0.302 0.663
Living with the child's father22 526reference reference 
Other family situation19941.110.91–1.34 0.940.71–1.25 
Mother's BMI <0.001 0.261
<2034251.120.96–1.31 1.240.99–1.54 
20–24.913 699reference reference 
25–29.953461.161.02–1.33 0.990.81–1.21 
≥3020501.461.23–1.74 1.010.76–1.35 
Grandmother's educational level (years) 0.019 0.226
9–1014 654reference reference 
11–1389581.090.97–1.22 0.970.82–1.15 
≥149080.690.49–0.97 0.600.34–1.07 
Grandfather's educational level (years) 0.885 0.507
9–1013 808reference reference 
11–1399360.980.88–1.10 0.910.77–1.07 
≥147761.050.76–1.44 0.920.54–1.55 
Grandmother's marital status 0.644 0.761
Married17 283reference reference 
Divorced/widowed8370.880.65–1.20 1.070.71–1.61 
Unmarried64001.030.90–1.18 1.070.88–1.30 
Grandmother's age when giving birth (years) 0.018 0.560
13–194001.180.79–1.77 0.790.40–1.55 
20–2613 579reference reference 
27–3386241.151.02–1.30 1.030.86–1.24 
≥3419170.850.68–1.07 0.830.60–1.17 
Grandmother's parity 0.357 0.260
No previous children9233reference reference 
Previous children15 2871.060.94–1.19 1.110.93–1.33 
Grandparents’ country of origin 0.155 0.985
Both Nordic23 725reference reference 
One or both non-Nordic7950.790.57–1.09 1.000.64–1.55 

Intergenerational effects of preterm birth and reduced intrauterine growth

In 3Table 3, the intergenerational effects of preterm birth and SGA on all eligible mother–offspring pairs (n= 38 720) and all pairs for which complete data on all background variables was available (n= 24 520) are presented. Preterm birth seemed to be more common in children whose mothers were born preterm or SGA, compared with mothers born at term or AGA, but when the analyses were restricted to include only the mothers with no missing data on the background variables, no significant intergenerational effect of preterm birth was present. Both the analyses performed on all mother–offspring pairs as well as the pairs with complete information on background variables showed an intergenerational effect of SGA, while preterm birth in the mothers was not of significant influence on the occurrence of SGA in the child. The analyses made on mother–offspring pairs with complete data further revealed that adjustments for current and childhood maternal socio-economic characteristics as well as BMI and smoking habits during early pregnancy did not substantially change the intergenerational effects found. Among mothers aged ≥25 years when giving birth, additional adjustments were made for the mother's own educational level and marital status at 25, but as these additional variables did not substantially influence the odds ratios presented (data not shown), they were not included in subsequent analyses. Also, the results of the analyses presented in Table 3 did not change substantially when restricted to only include mother–offspring pairs in which the mother also was first-born (n= 16 677 and 9233 for all eligible pairs and pairs with complete information on background variables, respectively), or if both preterm birth and SGA in the mothers were simultaneously adjusted for (i.e. included in the same model) (data not shown). In addition, as the SGA standard used was created in 1996,33 we computed an ‘adjusted SGA’ in which the mother's birthweight for gestational age was adjusted for the mean increase in birthweight between 1973–75 and 1996 (the procedure is explained in a previous study2), and reanalysed the corresponding analyses in Table 3. However, although the prevalence of SGA among the mothers was somewhat decreased (to 4.2 and 4.0% for all eligible pairs and pairs with complete information on background variables, respectively), the results of these analyses did not differ substantially from the results presented (data not shown).

Table 3.  Intergenerational effects of preterm birth and SGA in the mother–offspring pairs
 All eligible mother–offspring pairs (n= 38 720)All mother–offspring pairs with complete data on all background variables (n= 24 520)
 Crude analysesCrude analysesAdjusted analyses*
 n**OR***95% CIPn**OR***95% CIPOR***95% CIP
  • *

    Adjusted for the mother's smoking habits, cohabitation status, BMI, and age at the time of pregnancy. Adjustments were also made for the grandparents’ educational levels, grandmother's marital status, parity, and age when giving birth to the mothers of the study, as well as for the grandparents’ country of origin.

  • **

    Number of mothers with outcome in the category.

  • ***

    Odds ratio (logistic regression analysis).

Child born preterm 
Mother born preterm1171.311.08–1.590.006601.230.94–1.610.1301.240.95–1.620.116
Mother born SGA1641.231.04–1.450.015981.281.03–1.580.0251.301.05–1.610.017
Child born SGA 
Mother born preterm501.290.96–1.720.088231.090.72–1.660.6861.080.70–1.640.736
Mother born SGA1442.792.32–3.34<0.001822.762.17–3.50<0.0012.682.11–3.41<0.001

In order to evaluate the effect of the mother's gestational length as well as the mother's birthweight for gestational length (expressed in SDs) on the occurrence of preterm birth and SGA in the offspring in more detail, additional analyses were performed. These models were then evaluated against the models in Table 3 by comparing the −2ln(likelihood). 1Figure 1 shows that mothers who were themselves born SGA (i.e. whose birthweight for gestational age was <−2 SD below the Swedish standard) still had markedly higher odds ratios for giving birth to SGA children, compared with the reference category, but a positive effect was also present among mothers whose birthweight for gestational age was between −2 and −1.01 SD, as well as between −1 and −0.01 SD, respectively. These analyses further revealed that women whose birthweight for gestational age was ≥+1 SD were at somewhat reduced risk for giving birth to SGA children. A similar pattern, although less marked, was evident when analysing the impact of the mothers’ birthweight for gestational length on the occurrence of preterm birth in the children (2Figure 2). Both Figures 1 and 2 show that adjustments for current and childhood maternal socio-economic characteristics as well as BMI and smoking habits during early pregnancy did not substantially change the results.

image

Figure 1. Mother's intrauterine growth in relation to giving birth to SGA children.

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image

Figure 2. Mother's intrauterine growth in relation to giving birth to preterm children.

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Because of the relatively strong negative association evident in Figure 1, the relation between the mothers’ intrauterine growth and the first-born child's intrauterine growth was analysed (4Table 4). The table shows evidence of a positive association; i.e. the lower the birthweight for gestational age of the mother, the lower the birthweight for gestational age of the first-born child, and vice versa. We also performed more detailed analyses on the children whose intrauterine growth was reduced but who did not meet the criterion of being born SGA (3Figure 3). Figure 3 shows that although children born SGA were more common among mothers who were themselves born SGA, and among mothers whose birthweight for gestational age was between −2 and −1.01 SD, as well as between −1 and −0.01 SD, respectively, these mothers also had higher odds ratios for giving birth to children whose birthweight for gestational age was between −2 and −1.01 SD, as well as between −1 and −0.01 SD.

Table 4.  Mother's intrauterine growth in relation to the first-born child's intrauterine growth. Values are presented as percentages*
 First-born child's birth weight for gestational age (SDs)** 
 <−2−2 to −1.01−1 to −0.010 to +1+1.01 to +2>+2Total (n)
  1. *n= 38 720 (i.e. all eligible mother–offspring pairs).

  2. **First-born child's birthweight for gestational age (in SDs), standardised for the Swedish standard.33

  3. ***Mother's birthweight for gestational age (in SDs), standardised for the Swedish standard.33

Mother's birthweight for gestational age (SDs)*** 
<−26.726.339.321.15.41.22135 (100%)
−2 to −1.014.121.140.626.66.51.18230 (100%)
−1 to −0.012.714.939.432.09.41.514 295 (100%)
0 to +11.610.434.537.213.23.09855 (100%)
+1.01 to +21.06.829.940.617.14.63302 (100%)
>+21.06.824.738.022.17.4903 (100%)
Total (n)1070573714 42812 569406285438 720 (100%)
image

Figure 3. Mother's intrauterine growth in relation to giving birth to children with reduced intrauterine growth.

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In order to evaluate the models described above, they were tested for interactions. Although these additional analyses revealed no significant interaction (i.e. P≤ 0.01), there was some evidence (P= 0.017) that the mother's age when giving birth was a modifying effect when the relation between the mother's birthweight for gestational age and the occurrence of SGA in the child was examined. The odds ratio for giving birth to SGA children among mothers whose intrauterine growth was restricted seemed to increase with increasing maternal age.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgements
  9. References

Unlike most previous studies, we had the opportunity to account not only for the mother's current socio-economic factors but also for the socio-economic situation during her own childhood as well as her smoking habits and BMI during early pregnancy when investigating the intergenerational effects of preterm birth and reduced intrauterine growth (i.e. SGA). The present study is also one of the largest studies to date, which enables us to make more precise estimates of the intergenerational effects investigated than has previously been possible. In the present study, the estimated risk of preterm birth in the offspring was about 30% higher among Swedish mothers who themselves were born preterm, compared with mothers born at term. This effect was no longer significant once those with incomplete data were removed from the analyses and the odds ratio was adjusted for socio-economic factors. Increased risk, although not always statistically significant, has been suggested in several studies.16,17,19,20 Klebanoff et al.15 reported a reduced risk (OR = 0.53) in a smaller Swedish study, but, due to the wide confidence interval, their risk estimate did not differ from the risk estimate and corresponding confidence interval demonstrated in the present study. Furthermore, we found no significant increases in risk for SGA in the child among mothers who were born preterm, which is in line with previously reported results.15,16,20 The results of our study showed an intergenerational effect of SGA, but also that SGA in the mother had a positive effect on the occurrence of preterm birth in the offspring, which is in agreement with previously reported results.12–14,20 Klebanoff et al.15 reported a significantly higher unadjusted odds ratio for giving birth to preterm children in mothers who themselves were born SGA (OR 2.96, 95% CI 1.47–5.94). However, the odds ratio in their study became markedly lower when adjustments were made for maternal weight and smoking habits as well as marital status, and the adjusted analysis did not differ from the one presented in our study. In addition, we found evidence that the mothers whose intrauterine growth was reduced but who did not meet the criterion of being born SGA also were at higher risk of giving birth to preterm and SGA children, but also to children whose intrauterine growth was reduced but who did not meet the criterion of being born SGA.

There seems to be a relationship between preterm birth and SGA among the mothers and subsequent socio-economic situation in adult life. As expected from previous results,8,21 the mothers’ pre-pregnancy BMI seemed to be related to preterm birth in the offspring and there was also some evidence that underweight mothers were more likely to give birth to SGA children. Mothers who were themselves born SGA also seem to be more likely to be smokers in adult life, compared with mothers who were born AGA, and they also seem to have a lower BMI. These associations are also apparent in other studies.2,5,11,15,16 Mothers who were smokers have previously been shown to be more likely to give birth to preterm or growth-restricted children.8,21,22 We found that the mother's smoking habits were associated with SGA but not preterm birth in the offspring. The mother's educational level at 25 years of age was found to be related to SGA in the offspring, which has also been shown in previous studies,2,8 whereas the mother's marital status and cohabitation status seemed to be of minor importance in affecting the outcomes studied, although these associations were found in earlier research.2,21 A possible reason for these discrepancies might be that our population was smaller than in some of the previous studies;2,8,21 for instance, in the study by Clausson et al.8 the population consisted of nearly 100 000 women. Other explanations might be that the mothers in the present study were relatively young (i.e. 25–27 years at the end of follow up), compared with the mothers in other studies, or the fact that we were able to adjust the relationships studied for the socio-economic characteristics during the childhood of the mothers. Earlier research has shown an association between maternal socio-economic situation during childhood and offspring birthweight, independent of the mother's current socio-economic position.25 In the present study, preterm birth (but not SGA) in the offspring was related to the mothers’ socio-economic characteristics during childhood, even after adjustments were made for the mother's current socio-economic situation, smoking habits, and BMI.

Although some of the background characteristics studied, such as the mother's smoking habits, were significant confounding variables, they had notably little impact on the intergenerational effects. This could imply that the factors adjusted for in this study did not capture the whole social panorama. For instance, we were not able to investigate the effect of behavioural factors such as maternal use of drugs, which is known to increase the occurrence of preterm birth and SGA in the offspring.22 However, there is growing evidence that the intergenerational effects of preterm birth and SGA (as well as birthweight and gestational length) persist even after adjustments are made for current socio-economic status of the mother and/or father.10–13,16,19,23,24 Some studies have also indicated that the mother's smoking habits or weight/BMI during pregnancy, or her socio-economic situation during childhood, are not likely to explain the intergenerational effects found.12,14–16,23,24 Thus, it seems unlikely that the intergenerational associations of preterm birth and SGA are entirely explained by socio-economic characteristics or BMI and smoking habits. It has been suggested that the intergenerational effects of birthweight and gestational age might be explained by a genetic mechanism.17,18,35 Another explanation could be that the mothers were exposed to some unknown factor before they were born, a factor that caused them to be SGA or preterm at birth and also predisposed their offspring to be born SGA or preterm.

Although the overall information on gestational length and birthweight recorded in the MBR has been evaluated and found to be reliable,26,27 evaluations of the registry have shown evidence of measurement errors in both extremes of the distributions of birthweight and gestational length, respectively.26,36 In the present study, we have made efforts to exclude misreported and/or wrongly entered values from the dataset, and also chose to categorise the data in larger categories instead of basing the analyses on individual values, which decreases the influence of possible incorrect values on the results. However, there may be other sources of error that affect the information regarding preterm birth and SGA. The results show that across the two generations studied, the prevalence of preterm birth has increased from 3.7% in the mothers to 6.4% in the children, while the prevalence of SGA decreased from 5.5 to 2.8%. Since the middle of the 1980s, the gestational length has been measured by means of ultrasound examinations predominantly made at 16–18 weeks of gestation.27 As the mothers of this study were born in 1973–75, their gestational age was estimated by using last normal menstrual period dating, which has been evaluated to produce, on average, two to three days longer estimates than the more precise ultrasound examination.37 Some researchers believe that the difference in estimates between the two methods is involved in explaining the recent trend of increased prevalence of preterm births.37 If correct, this means that in the present study, there are some mothers who ought to have been classified as preterm but were wrongly classified as full-term (i.e. ‘false negatives’). As a result, the analyses involving the mothers’ gestational length may be slightly conservative. An explanation to the decline in the prevalence of SGA could be that the current Swedish SGA standard is based on intrauterine measures of children born in the middle of the 1990s. As the mean birthweight for children born in 1973–75 was 50–60 g lower compared with children born in 1996,38 this implies that there may be some ‘false positives’ among the SGA mothers. However, although adjusting for this increase in birthweight decreased the prevalence of SGA among the mothers to 4.2%, the intergenerational effect of SGA did not substantially change. Thus, we believe that this increase in birthweight is of minor importance to the conclusions drawn from this study.

These differences in prevalence could also theoretically be due to the fact that not all mothers were first-born or due to biases arising from missing values. However, neither the results of the analyses in which only the first-born-mother–offspring pairs were included nor those of analyses that included all eligible mother–offspring pairs differed substantially from the results on the pairs with complete data on all background variables. Missing values on the child's gestational length and/or birthweight were more common among teenage mothers and mothers with lower educational levels. This could also have biased the results. However, we believe that this possible bias would have had little effect on the results as the amount of missing values was small (less than 1%). As some of the previous research in this area has indicated that teenage mothers and mothers with low education tend to give birth to children with lower birthweights and length of gestation,2,21,22 our results could be slightly more conservative than if there were no missing values on birthweight and gestational length. During the period of study, there have been some other changes in obstetric practice in Sweden that affect the information in the MBR, and these may have affected the results of this study. For example, caesarean sections, including caesarean sections for fetal indication at an early gestational age, have become more widely used in the 1990s compared with the 1970s; 5.3–7.0% of the children born in 1973–75 were delivered by means of caesarean section, compared with 11.3–14.8% in 1986–2000.38

Conclusions

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgements
  9. References

The present study showed evidence of intergenerational effects of both preterm birth and reduced intrauterine growth (i.e. SGA). There was also evidence that the mothers whose intrauterine growth was moderately reduced but who did not meet the criterion of being born SGA were at higher risk of giving birth not only to preterm and SGA children but also to children with moderately reduced intrauterine growth. Preterm birth and SGA in the mothers were also associated with their subsequent socio-economic situations. Mothers who were themselves born SGA also seemed to be more likely to be smokers and to have a lower BMI in early pregnancy. In addition, the mother's current and childhood socio-economic factors as well as BMI and smoking habits influenced the outcome of her pregnancy. However, these factors did not account for the intergenerational effects found.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgements
  9. References

This study was supported by grants from the Swedish Council for Working Life and Social Research and the Medical Research Council of Southeast Sweden (FORSS). The authors would like to thank the National Board of Health and Welfare and Statistics Sweden for help and access to the registries.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Conclusions
  8. Acknowledgements
  9. References
  • 1
    Barker DJP. Mothers, Babies and Health in Later Life. Edinburgh, Scotland, UK: Churchill Livingstone, 1998.
  • 2
    Ekholm K, Carstensen J, Finnström O, Sydsjö G. The probability of giving birth among women who were born preterm or with impaired fetal growth: a Swedish population-based registry study. Am J Epidemiol 2005;161: 72533.
  • 3
    Ericson A, Källén B. Very low birthweight boys at the age of 19. Arch Dis Child Fetal Neonatal Ed 1998;78: F1714.
  • 4
    Finnstöm O, Gäddlin PO, Leijon I, Samuelsson S, Wadsby M. Very-low-birth-weight children at school age: academic achievement, behavior and self-esteem and relation to risk factors. J Matern Fetal Neonatal Med 2003;14: 7584.
  • 5
    Hack M, Flannery DJ, Schluchter M, Cartar L, Borawski E, Klein N. Outcomes in young adulthood for very-low-birth-weight infants. N Engl J Med 2002;346: 14957.
  • 6
    Ibanez L, Potau N, Enriquez G, De Zegher F. Reduced uterine and ovarian size in adolescent girls born small for gestational age. Pediatr Res 2000;47: 5757.
  • 7
    Ibanez L, Potau N, Ferrer A, Rodriguez-Hierro F, Marcos MV, De Zegher F. Reduced ovulation rate in adolescent girls born small for gestational age. J Clin Endocrinol Metab 2002;87: 33913.
  • 8
    Clausson B, Cnattingius S, Axelsson O. Preterm and term births of small for gestational age infants: a population-based study of risk factors among nulliparous women. Br J Obstet Gynaecol 1998;105: 101117.
  • 9
    Skjaerven R, Wilcox AJ, Oyen N, Magnus P. Mothers’ birth weight and survival of their offspring: population based study. BMJ 1997;314: 137680.
  • 10
    Alberman E, Emanuel I, Filakti H, Evans SJ. The contrasting effects of parental birthweight and gestational age on the birthweight of offspring. Paediatr Perinat Epidemiol 1992;6: 13444.
  • 11
    Collins JW Jr, David RJ, Prachand NG, Pierce ML. Low birth weight across generations. Matern Child Health J 2003;7: 22937.
  • 12
    Emanuel I, Kimpo C, Moceri V. The association of maternal growth and socio-economic measures with infant birthweight in four ethnic groups. Int J Epidemiol 2004;33: 123642.
  • 13
    Hyppönen E, Power C, Smith GD. Parental growth at different life stages and offspring birthweight: an intergenerational cohort study. Paediatr Perinat Epidemiol 2004;18: 16877.
  • 14
    Jaquet D, Swaminathan S, Alexander GR, Czernichow P, Collin D, Salihu HM, et al. Significant paternal contribution to the risk of small for gestational age. BJOG 2005;112: 1539.
  • 15
    Klebanoff MA, Meirik O, Berendes HW. Second-generation consequences of small-for-dates birth. Pediatrics 1989;84: 3437.
  • 16
    Klebanoff MA, Schulsinger C, Mednick BR, Secher NJ. Preterm and small-for-gestational-age birth across generations. Am J Obstet Gynecol 1997;176: 5216.
  • 17
    Magnus P, Bakketeig LS, Skjaerven R. Correlations of birth weight and gestational age across generations. Ann Hum Biol 1993;20: 2318.
  • 18
    Magnus P, Gjessing HK, Skrondal A, Skjaerven R. Paternal contribution to birth weight. J Epidemiol Community Health 2001;55: 8737.
  • 19
    Porter TF, Fraser AM, Hunter CY, Ward RH, Varner MW. The risk of preterm birth across generations. Obstet Gynecol 1997;90: 637.
  • 20
    Winkvist A, Mogren I, Högberg U. Familial patterns in birth characteristics: impact on individual and population risks. Int J Epidemiol 1998;27: 24854.
  • 21
    Ancel PY, Saurel-Cubizolles MJ, Di Renzo GC, Papiernik E, Breart G. Very and moderate preterm births: are the risk factors different? Br J Obstet Gynaecol 1999;106: 116270.
  • 22
    Haram K, Mortensen JH, Wollen AL. Preterm delivery: an overview. Acta Obstet Gynecol Scand 2003;82: 687704.
  • 23
    Hennessy E, Alberman E. Intergenerational influences affecting birth outcome. I. Birthweight for gestational age in the children of the 1958 British birth cohort. Paediatr Perinat Epidemiol 1998;12(Suppl 1):4560.
  • 24
    Hennessy E, Alberman E. Intergenerational influences affecting birth outcome. II. Preterm delivery and gestational age in the children of the 1958 British birth cohort. Paediatr Perinat Epidemiol 1998;12(Suppl 1):6175.
  • 25
    Emanuel I. Maternal health during childhood and later reproductive performance. Ann N Y Acad Sci 1986;477: 2739.
  • 26
    Cnattingius S, Ericson A, Gunnarskog J, Källén B. A quality study of a medical birth registry. Scand J Soc Med 1990;18: 1438.
  • 27
    Centre for Epidemiology, National Board of Health and Welfare. The Swedish Medical Birth Register: A Summary of Content and Quality. Stockholm, Sweden: National Board of Health and Welfare, 2003 [www.sos.se/fulltext/112/2003-112-3/2003-112-3.pdf]. Accessed 16 February 2004.
  • 28
    Statistics Sweden. A New Total Population Register System: More Possibilities and Better Quality. Örebro, Sweden: Statistics Sweden, 2002. Serial no. 2002:2.
  • 29
    Centre for Epidemiology, National Board of Health and Welfare. Causes of Death 2001. Stockholm, Sweden: National Board of Health and Welfare, 2003 [www.sos.se/FULLTEXT/42/2003-42-5/2003-42-5.pdf]. Accessed 16 February 2004.
  • 30
    Statistics Sweden. Multi-generation Register 2002: A Description of Contents and Quality. Örebro, Sweden: Statistics Sweden, 2003. Serial no. 2003:5.
  • 31
    Statistics Sweden. Educational Attainment of the Population 2002. Örebro, Sweden: Statistics Sweden, 2003. Publication no. UF0506. [www.scb.se]. Accessed 16 February 2004.
  • 32
    Statistics Sweden. Population and Housing Census 1970 (SOS). Part 12. Report on the Planning and Processing of the Population and Housing Census 1970. Stockholm, Sweden: National Central Bureau of Statistics, 1974.
  • 33
    Marsal K, Persson PH, Larsen T, Lilja H, Selbing A, Sultan B. Intrauterine growth curves based on ultrasonically estimated foetal weights. Acta Paediatr 1996;85: 8438.
  • 34
    Statistics Sweden. Population and Housing Census 1970. Part 13. Economic Activity and Education. Definitions, Comparability, Development, etc. Stockholm, Sweden: National Central Bureau of Statistics, 1975.
  • 35
    Varner MW, Esplin MS. Current understanding of genetic factors in preterm birth. BJOG 2005;112(Suppl 1):2831.
  • 36
    Ericson A, Gunnarskog J, Källén B, Olausson PO. A registry study of very low birthweight liveborn infants in Sweden, 1973–1988. Acta Obstet Gynecol Scand 1992;71: 10411.
  • 37
    Morin I, Morin L, Zhang X, Platt RW, Blondel B, Breart G, et al. Determinants and consequences of discrepancies in menstrual and ultrasonographic gestational age estimates. BJOG 2005;112: 14552.
  • 38
    Centre for Epidemiology, National Board of Health and Welfare. Nordic Birth Statistics/Sweden 1973–2000. Stockholm, Sweden: National Board of Health and Welfare, 2004. [www.sos.se/epc/fodelse/mfrfiler/nordisk.htm]. Accessed 30 September 2005.