Are risk factors the same for small for gestational age versus other preterm births?

Are risk factors the same for small for gestational age versus other preterm births? Jennifer A. Zeitlin, DSc,a Pierre-Yves Ancel, MD,a Marie-Josèphe Saurel-Cubizolles, PhD,a and Emile Papiernik, MDb Paris, France

OBJECTIVES: This article explores whether the impact of social and demographic risk factors for preterm birth differs for small for gestational age preterm births versus other preterm births. STUDY DESIGN: This was a European case control study of the determinants of preterm birth (4700 cases and 6460 controls). Small for gestational age and non-small for gestational age preterm births were compared with a control group of term births; relationships were explored further by stratifying preterm births into subgroups by mode of onset, the presence of hypertension, and gestational age. RESULTS: Of the social and demographic risk factors for preterm birth identified in this sample, high maternal age, smoking, and low and high maternal body mass index have a stronger effect on small for gestational age preterm births. In contrast, obstetric history, maternal education, and marital status have similar effects regardless of birth weight. Hypertension during pregnancy is strongly associated with small for gestational age preterm birth and contributes to an explanation of observed differences. CONCLUSIONS: These results underline the importance of considering fetal growth restriction in the analysis of risk factors for preterm birth. (Am J Obstet Gynecol 2001;185:208-15.)

Key words: Intrauterine growth restriction, preterm birth, risk factors, small for gestational age

Because preterm birth has multiple causes, it is postulated that social and demographic risk factors will differ in relation to underlying biologic processes or associated medical conditions. The interaction between demographic and social characteristics and medical risk factors has been explored in relation to types of preterm birth (preterm labor, premature rupture of membranes, and medical conditions)1, 2 and to vaginal infections.3 The investigation of demographic and social risk factors in relation to underlying etiologic hypotheses can clarify why they are related to preterm birth (because of their relationship with infection, for instance) and why these risk factors have a greater or lesser impact in different populations (where the prevalence of medical risk factors or types of preterm birth vary). Intrauterine growth restriction is one factor that affects the risk of preterm birth. Impaired growth is associFrom INSERM, Epidemiological Research Unit on Perinatal and Women’s Health (U149),a and Port-Royal Maternity Hospital.b The EUROPOP study was financed by the European Union BIOMED project BMH1-CT94-1041. Received August 25, 2000; revised January 11, 2001; accepted January 31, 2001. Reprint requests: Jennifer Zeitlin, U149, INSERM, 123 bd Port-Royal, 75014 Paris, France. Copyright © 2001 by Mosby, Inc. 0002-9378/2001 $35.00 + 0 6/1/114869 doi:10.1067/mob.2001.114869

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ated with maternal conditions such as hypertension, which lead to medically indicated births, and growth restriction is itself a common indication for induction before term. Evidence is also accumulating for an association between intrauterine growth restriction and spontaneous preterm birth: preterm infants born after spontaneous preterm labor or preterm premature rupture of membranes appear to deviate from normal fetal growth patterns.4-6 These results suggest that growth restriction may be related to a distinct set of mechanisms leading to preterm birth, either directly or indirectly, and that consequently risk factors for preterm birth associated with growth restriction could differ from risk factors for other preterm births. Existing research on risk factors for small for gestational age (SGA) preterm infants is scant. Some studies have contrasted risk factors for preterm birth with those for SGA births, but they have not considered SGA preterm infants separately: they are either excluded from analyses7, 8 or included in analyses of both outcomes.9, 10 This analysis tested whether the impact of common social and demographic risk factors for preterm birth differs for SGA preterm births and for non-SGA preterm births with the use of data on 4700 preterm births from a large case control study, the European Program of Occupational Risks and Pregnancy Outcome (EUROPOP). We explored these relationships further within sub-

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groups of preterm births in which intrauterine growth retardation is more common: indicated preterm deliveries, those associated with hypertension, and early preterm births. Material and methods Data. The EUROPOP case control study was carried out in 17 European countries with the principal aim of exploring the relationship between occupational risk factors and preterm birth. Countries that participated in the study are the Czech Republic, Finland, France, Germany, Greece, Hungary, Ireland, Italy, The Netherlands, Poland, Romania, Russia, Scotland (UK), Slovenia, Spain, Sweden, and Turkey. Data from Turkey were not used in this analysis because the control population was incomplete. The study and its methods have been described elsewhere.11 Cases were all preterm singleton live and stillbirths between 22 and 36 completed weeks of amenorrhea born in participating maternity hospitals during the study period. The control group included every 10th singleton live birth or stillbirth at 37 or more completed weeks of gestation in each maternity unit. The same questionnaire was used in all countries to gather information about the mother, her pregnancy, and the newborn. Interviewers questioned the mother and abstracted information from her medical records. Gestational age was based on the best estimate of gestational age used by the obstetric team. These estimates were based on ultrasound measures, last menstrual period, and neonatal assessments. Of the approximately 90% of the sample with data on the timing of the first ultrasound scan (89.5% of the control group; 87.2% of cases), 64% of the case group and 65% of the control group had an ultrasound examination before 15 weeks, and 87% of the case group and 86% of the control group had at least one ultrasound examination before 21 weeks. The analyses presented in the following text use data on nonmalformed live born infants. Fifty-three members of the case group and 44 members of the control group were excluded because gestational age estimations were discordant and there was a doubt about their case or control status. Birth weight outliers were identified by detecting extremes of each birth weight distribution by gestational age. Data on birth weight were already missing for 13 members of the control group and 39 members of the case group, and the cleaning process resulted in a further 35 exclusions for the case group and 22 for the control group. Maternal height, weight, and parity, which are necessary for developing the growth reference standards (see following text), were missing for 5% of the case group and 5% of the control group. The final sample comprised 4700 members in the case group and 7827 members in the control group.

Zeitlin et al 209

Identifying growth-restricted infants. We adopted the conventional 10th percentile cutoff point to identify infants who were SGA with the adjustable fetal weight standards developed by Gardosi et al,12 which are based on the fetal growth formula of Hadlock et al13 established from ultrasound measures from 10 to 40 weeks of gestation. Gardosi et al12 used the birth weight distributions in a population of singleton births from Nottingham (UK) to develop proportional growth curves for the 10th, 50th, and 90th percentiles. These reference curves were adjusted for biologic characteristics that are known to affect birth weight: maternal parity, weight, height, and sex of the newborn, to improve the accuracy of thresholds for identifying growth restriction.14 Linear multiple regression was used to estimate the impact of these factors on average birth weight at term, and these coefficients were applied to the proportional growth curves to derive customized reference percentiles at all gestational ages. This approach made it possible to take into consideration the diversity of the populations included in the EUROPOP study. Analyses of birth weight in the EUROPOP sample including comparisons with the model of Gardosi et al have been published elsewhere.6 In this article, we use the term intrauterine growth restriction to refer to the phenomenon of restricted growth in relation to the genetic potential of each infant. The term small for gestational age refers to infants with a birth weight under the 10th percentile of reference standards, a common proxy measure of intrauterine growth restriction in a population of births. Variables selected for analysis. This analysis focused on variations in the impact of known risk factors for preterm birth by the SGA status of the infant. The variables selected for this analysis were those that were significant predictors of preterm birth in this sample11: maternal age (coded in 5-year intervals), obstetric history (coded as primiparous, multiparous with no adverse outcomes, multiparous with a history of first-trimester miscarriages or abortions, and multiparous with a history of secondtrimester miscarriages, abortions, or preterm birth), marital status (married, unmarried couple, single), age at completion of schooling, smoking status in the third trimester of pregnancy, and body mass index (coded to look at impact of thinness [<18.3] and obesity [>28.8]). The impact of body mass index was affected by the adjustment of thresholds for identifying SGA infants by maternal weight and height, as described previously. In the absence of adjustment, maternal body mass index can be associated with low birth weight, because smaller mothers naturally tend to have smaller babies and because thinness or obesity are risk factors for growth restriction. In these analyses, however, only the latter effect is measured, because the natural relationship between mater-

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Table I. Association between sociodemographic characteristics and preterm birth for SGA and non-SGA preterm births compared with a control group of term births Distribution of risk factors (%)

Adjusted odds ratios

Preterm births Sociodemographic characteristics

Preterm births

Control group (n = 7821) Non-SGA (n = 3631) SGA (n = 1076) Non-SGA

Obstetric history Primigravid women No previous problem First-trimester miscarriage/abortion Second-trimester miscarriage/abortion Maternal age (y) <20 20-29 30-34 35-39 40+ Marital status Married Unmarried couple Single mother Age at end of schooling (y) <16 16-17 18-20 21 + Smoking in third trimester Does not smoke 1-9 cigarettes per day 10-19 cigarettes per day 20+ cigarettes per day Body mass index (kg/m2) Very thin <18.3 Normal/overweight 18.3-28.8 Obese >28.8

SGA

P value*

38.3 28.6 23.6 9.5

38.1 19.2 22.3 20.4

38.8 18.3 21.9 21.0

1.52† 1 1.56† 3.52†

1.91† 1 1.68† 3.72†

NS

4.5 56.0 27.2 10.2 2.1

6.0 52.7 25.3 12.4 3.6

5.2 46.9 27.9 15.8 4.1

1.17 1 0.97 1.19‡ 1.54†

0.99 1 1.26† 1.76† 2.09†

.003

79.1 18.6 5.3

75.0 18.0 7.0

76.6 16.4 7.0

1 1.23† 1.25‡

1 1.08 1.20

NS

14.5 20.4 36.6 28.5

17.8 19.8 37.4 25.0

20.7 20.3 35.2 23.8

1.48† 1.24† 1.19† 1

1.89† 1.47† 1.20§ 1

NS

82.5 9.6 4.1 1.8

77.8 11.3 8.5 2.4

75.3 11.1 10.2 3.4

1 1.14 1.29† 1.24

1 1.21 1.72† 2.08†

.014

4.8 90.4 4.8

6.7 88.2 5.1

7.8 85.1 7.1

1.34† 1 1.11

1.69† 1 1.58†

.014

Odds ratios are adjusted for all sociodemographic characteristics shown in Table I and country of residence. NS, Not significant. *P value indicates the statistical significance of difference in odds ratios between the 2 groups for a given risk factor. †,‡,§P values indicating statistical significance of odds ratio measuring impact of risk factor on preterm birth, where †P < .01, ‡P < .05, and § = .054.

nal size and infant size has been taken into account in the identification of SGA infants. Statistical analysis. This analysis separated preterm births into those in which the infant was SGA and those in which the infant was not SGA and compared these 2 subgroups with a control group composed of infants born at term. The choice of control group was made to contrast odds ratios derived from these analyses with those in the literature on risk factors for preterm birth. In particular, the decision was made not to exclude SGA births at term from the control group. The effect of removing SGA infants had no impact on the relationship between the nonSGA and SGA groups and only modified the odds ratio by a constant factor over all groups. Preterm infants were classified by SGA status and were then compared with the control group of term infants with multinomial logistic regression (polytomous logistic regression). Equality of coefficients for the SGA and nonSGA groups was tested with the Wald statistic, constructed from the estimated covariance matrix. Statistical analysis

was done with Stata Statistical Software (release 5.0, Stata Corporation, 1997). Preterm births were stratified into subgroups by mode of onset, the presence of hypertension, and gestational age (early and moderate preterm births). For analyses by mode of onset, we distinguished between preterm births occurring after spontaneous preterm labor or premature rupture of membranes and births induced before term for medical reasons other than premature rupture of membranes (n = 1279). Early preterm births were defined as births before 33 weeks of gestation. Results Table I presents the distribution of sociodemographic risk factors for the control group, SGA preterm infants, and non-SGA preterm infants and odds ratios for each item, adjusted by all other sociodemographic risk factors and country of residence. The final column presents P values from the Wald test of the equality of coefficients

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Table II. Association between medical problems during pregnancy and preterm birth for SGA and non-SGA preterm births compared with a control group of term births Distribution of risk factors (%)

Adjusted odds ratios

Preterm births Medical problems during pregnancy Hypertension No diagnosis of hypertension Without proteinuria With proteinuria Hemorrhaging No hemorrhaging From abruptio placentae From placenta previa From other causes Diabetes No diagnosis of diabetes Gestational diabetes Pre-existing diabetes Proven urinary tract infection No Yes Anemia No Yes Uterine malformation No Yes Cervical incompetence No Yes

Preterm births

Control group (n = 7821) Non-SGA (n = 3631) SGA (n = 1076) Non-SGA

SGA

P value*

6.2 4.6

16.4 16.9

1 1.43† 3.11†

1 5.34† 17.51†

<.001

4.6 1.7

5.2 2.3 7.8

11.6 2.0 7.5

1 4.48† 5.51† 2.03†

1 12.01† 5.07† 2.07†

<.001

1.4 0.5 4.4

3.9 1.7

3.1 0.4

1 1.53† 3.84†

1 1.00 0.86

.002

2.4 0.4

8.9

9.2

1 1.13

1 1.30‡

NS

7.8

7.1

5.3

1 1.23‡

1 0.93

.08

6.0

2.1

1.8

1 3.57†

1 2.98†

NS

0.6

6.5

6.0

1 3.12‡

1 2.65‡

NS

2.1

Odds ratios are adjusted for all sociodemographic characteristics shown in Table I and country of residence. NS, Not significant. *P value indicates the statistical significance of difference in odds ratios between the 2 groups for a given risk factor. †,‡P values indicating statistical significance of odds ratio measuring impact of risk factor on preterm birth, where †P < .01 and ‡P < .05.

between the SGA and non-SGA groups. This tests the null hypothesis that each risk factor has the same impact for preterm birth associated with SGA and for other preterm births. The odds ratios for SGA preterm births and other preterm births were significantly different for 3 of the risk factors: maternal age (P = .003), smoking (P = .014), and body mass index (P = .014). High maternal age, smoking, and both low and high body mass index were associated with greater odds of SGA preterm birth compared with nonSGA preterm birth. A clearer dose-response relationship was also evident for amount smoked in the third trimester of pregnancy and the odds of having an SGA preterm birth. In contrast, the impact of obstetric history did not differ between the 2 groups, although this is a major risk factor for preterm birth. There are slight differences in the coefficients for education and marital status, but the hypothesis of equality of coefficients is not rejected. Table II compares the distribution of medical problems experienced during pregnancy between members of the control group and the 2 groups of preterm births and presents adjusted odds ratios for both groups. Hypertension during pregnancy and hemorrhaging associated with placental abruption were much stronger risk

factors for SGA preterm birth than non-SGA preterm birth (P < .001). Diabetes in pregnancy did not appear as a risk factor for SGA preterm birth (odds ratio = .86, not significant), whereas it was a significant risk factor for non-SGA preterm births (odds ratio = 3.84, P < .05). Other risk factors for preterm delivery such as hemorrhaging from placenta previa, uterine malformations, and cervical incompetence did not differ between the 2 groups, although they were all highly significant risk factors for preterm birth. Tables III, IV, and V stratify the sample of preterm births into subgroups to take into consideration known variability in the characteristics of SGA and non-SGA preterm births that could explain the differences seen in Table I. SGA births are more likely to be delivered by medical decision and to be associated with a diagnosis of hypertension. Early preterm births are more often SGA than moderate preterm births. Tables III and IV distinguish between preterm births induced for reasons other than premature rupture of membranes and those occurring after preterm premature rupture of membranes or spontaneous preterm labor (Table III) and between preterm births associated with hypertension and those with no diagnosis of hyper-

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Table III. Selected risk factors for induced and spontaneous preterm births by SGA status compared with a control group of term births Induced preterm births not associated with premature rupture of membranes

Spontaneous or premature rupture of membrane–associated births

Adjusted odds ratios Risk factors Maternal age (y) <20 20-29 30-34 35-39 40+ Smoking in third trimester Does not smoke 1-9 cigarettes per day 10+ cigarettes per day Body mass index (kg/m2) Very thin <18.3 Normal/overweight Obese >28.8

Adjusted odds ratios

SGA (n = 498)

P value*

Non-SGA (n = 2946)

SGA (n = 576)

P value*

1.04 1 1.02 1.53† 2.69†

1.22 1 1.45† 1.74† 2.25†

NS

1.18 1 0.98 1.10 1.29§

0.86 1 1.15 1.83‡ 2.01‡

.002

1 0.78 0.83

1 1.19 1.58†

.005

1 1.23† 1.37†

1 1.21 1.96†

.034

0.97 1 1.75†

1.23 1 1.84†

NS

1.41† 1 0.95

2.10† 1 1.38ll

.011

Non-SGA (n = 676)

Odds ratios are adjusted for all sociodemographic characteristics shown in Table I and country of residence. NS, Not significant. *P value indicates the statistical significance of difference in odds ratios between the 2 groups for a given risk factor. †,‡,§P values indicating statistical significance of odds ratio measuring impact of risk factor on preterm birth, where †P < .01, ‡P < .05, §P = .075, and llP = .088.

Table IV. Selected risk factors for preterm births associated with hypertension and preterm births not associated with hypertension by SGA status compared with a control group of term births Diagnosis of hypertension

No diagnosis of hypertension

Adjusted odds ratios Risk factors Maternal age (y) <20 20-29 30-34 35-39 40+ Smoking in third trimester Does not smoke 1-9 cigarettes per day 10+ cigarettes per day Body mass index (kg/m2) Very thin <18.3 Normal/overweight Obese >28.8

Adjusted odds ratios

Non-SGA (n = 392)

SGA (n = 377)

P value*

Non-SGA (n = 3330)

SGA (n = 714)

P value*

0.86 1 1.25 1.80† 4.14†

0.81 1 1.74† 1.80† 2.86†

NS

1.19 1 0.96 1.13 1.29§

1.04 1 1.08 1.79† 1.73‡

.008

1 0.74 0.85

1 0.95 1.03

NS

1 1.20‡ 1.34†

1 1.37 2.18†

.001

0.51 1 3.02†

0.87 1 2.71†

NS

1.42† 1 0.86

2.04† 1 1.06

.030

Odds ratios are adjusted for all sociodemographic characteristics shown in Table I and country of residence. NS, Not significant. *P value indicates the statistical significance of difference in odds ratios between the 2 groups for a given risk factor. †,‡,§P values indicating statistical significance of odds ratio measuring impact of risk factor on preterm birth, where †P < .01, ‡P < .05, and §P = .064.

tension during pregnancy (Table IV). These subgroups overlap, because 41% of induced pregnancies were associated with a diagnosis of hypertension, and 66% of the pregnancies associated with hypertension were induced. For spontaneous preterm births and preterm births not associated with hypertension, high maternal age, cigarette smoking, and low maternal body mass index were

stronger predictors of preterm birth associated with SGA than non-SGA preterm births. In contrast, obesity was significantly related to preterm birth only in the group of induced preterm births and the group of births associated with hypertension. In the group of pregnancies with a diagnosis of hypertension, the odds ratios for SGA and nonSGA preterm births associated with high maternal age

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Table V. Selected risk factors for moderate and very preterm birth by SGA status and presence of hypertension compared with a control group of term births Very preterm births <33 weeks

Moderate preterm births 33-36 weeks

Adjusted odds ratios Risk factors All preterm births Maternal age (y) <20 20-29 30-34 35-39 40+ Smoking in third trimester Does not smoke 1-9 cigarettes per day 10+ cigarettes per day Body mass index (kg/m2) Very thin <18.3 Normal/overweight Obese >28.8 Diagnosis of hypertension Maternal age (y) <20 20-29 30-34 35-39 40+ Smoking in third trimester Does not smoke 1-9 cigarettes per day 10+ cigarettes per day Body mass index (kg/m2) Very thin <18.3 Normal/overweight Obese >28.8 No hypertension Maternal age (y) <20 20-29 30-34 35-39 40+ Smoking in third trimester Does not smoke 1-9 cigarettes per day 10+ cigarettes per day Body mass index (kg/m2) Very thin <18.3 Normal/overweight Obese >28.8

Non-SGA

SGA

n = 888

n = 365

1.19 1 1.04 1.30‡ 1.42

1.12 1 1.26 2.01† 2.34†

1 1.05 1.64†

Adjusted odds ratios P value*

P value*

Non-SGA

SGA

n = 2743

n = 711

NS

1.16 1 0.96 1.15§ 1.61†

0.93 1 1.30† 1.67† 2.02†

.009

1 1.15 1.29

NS

1 1.17‡ 1.17ll

1 1.25 2.10†

<.001

1.43‡ 1 1.15 n = 83

1.38 1 2.11† n = 151

.04

1.32† 1 1.09 n = 309

1.89† 1 1.31 n = 206

.054

0.50 1 1.05 1.08 2.90‡

1.31 1 1.49¶ 2.04† 2.72†

NS

1.01 1 1.33# 2.05† 4.52†

0.53 1 1.96† 1.62‡ 2.93†

.08

1 0.93 0.96

1 1.17 0.85

NS

1 0.70 0.82

1 0.80 1.18

NS

0.51 1 2.72† n = 799

0.47 1 3.04† n = 213

.04

0.49 1 3.01† n = 2421

1.18 1 2.36† n = 501

NS

1.27 1 1.05 1.35‡ 1.35

1.00 1 1.11 2.09† 2.25‡

NS

1.17 1 0.94 1.06 1.31**

1.06 1 1.10 1.71† 1.61

.046

1 1.09 1.73†

1 1.17 1.58‡

NS

1 1.23‡ 1.22‡

1 1.46‡ 2.48†

.001

1.51† 1 0.93

2.01† 1 1.53

NS

1.41† 1 0.82

2.12† 1 0.89

.061

Odds ratios are adjusted for all sociodemographic characteristics shown in Table I and country of residence. NS, Not significant. *P value indicates the statistical significance of difference in odds ratios between the 2 groups for a given risk factor. †,‡,§, ll, ¶, #, **P values indicating statistical significance of odds ratio measuring impact of risk factor on preterm birth, where †P < .01, ‡P < .05, §P = .075, llP = .066, ¶P = .059, #P = .064, and **P = .075.

and obesity were very similar. Smoking during pregnancy was not a risk factor for prematurity associated with hypertension in either group. Table V presents the 3 sociodemographic risk factors for which significant differences were observed by the timing of the preterm birth. Because the presence of hypertension appears to mediate the impact of these risk factors (Table IV), this analysis was also done for preterm

births with a diagnosis of hypertension and those without. Significant differences between SGA and non-SGA births persisted for all 3 variables among moderate preterm births. The trends appeared to be different in the group of early preterm births. For instance, a high body mass index appeared to be associated only with SGA births, whereas the odds ratio for a low body mass index was similar in the 2 groups. However, once the presence of a di-

214 Zeitlin et al

agnosis of hypertension was taken into consideration, similar patterns were observed between early and moderate preterm births for maternal age and body mass index, although our power to detect differences was low in the group of early preterm births. The odds ratios associated with smoking were higher among moderate preterm births, but the small sample sizes among early preterm births complicated interpretation in this group. Analyses by the presence of hypertension yielded very similar results regardless of timing of birth. Comment This analysis shows that some common demographic and social risk factors for preterm birth do not have the same impact for SGA versus non-SGA preterm births, underlining the importance of considering intrauterine growth restriction in analyses of risk factors for preterm birth. In particular, maternal age over 35 years, smoking during pregnancy, and both low and high maternal body mass index are stronger risk factors for SGA preterm births than for other preterm births. The differences in medical risk factors between SGA and non-SGA preterm births in this sample were consistent with clinical expectations and other research.15-17 The most marked difference between the 2 groups was the presence of hypertension, which is a major correlate of preterm SGA birth: the odds ratio associated with hypertension during pregnancy for an SGA preterm birth is approximately 5 times greater than that for a non-SGA preterm birth. Hemorrhaging from placental abruption is also more frequently associated with SGA preterm birth, with an odds ratio 3 times greater. Also in line with expectations, diabetes is a greater risk factor for non-SGA preterm birth, a finding that reflects the increased growth associated with diabetes preventing SGA birth at any gestational age. Other medical risk factors that are significantly associated with preterm birth such as diabetes, placenta previa, or uterine malformations, however, do not differ in relation to low fetal weight. Because indicated preterm deliveries and delivery at a lower gestational age are more common among SGA preterm births6, 15 and because of the differences noted for the presence of hypertension, the impact of risk factors was tested within subgroups defined in relation to these characteristics. These analyses helped to clarify the observed associations. Older mothers are more likely to have indicated preterm births and to have pregnancies complicated by hypertension; this is one reason that high maternal age is more strongly related to SGA preterm births. In a similar manner, obesity is a risk factor for hypertension during pregnancy. Another distinguishing characteristic of the group of hypertension-related preterm births is that smoking during pregnancy is not a risk factor. The protective effect of smoking on hypertension during pregnancy18 could explain this result.

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The reduction in the incidence of hypertension among pregnant women who smoke could possibly lower the incidence of preterm birth associated with this condition for smokers. The differences in the impact of risk factors for SGA versus non-SGA preterm births are less apparent for preterm births associated with hypertension and induced preterm births. Maternal age is strongly associated with induced preterm births regardless of SGA status. The odds ratios for smoking and obesity were similar for SGA and non-SGA preterm births associated with hypertension. Once the differences caused by the presence of hypertension were taken into consideration, the trends observed for SGA and non-SGA preterm births were similar in the groups of moderate and early preterm births. These analyses by subgroup contribute to an understanding of some of the observed variation in the impact of risk factors for SGA versus non-SGA births. However, all differences, with the exception of obesity, were significant in the groups of spontaneous and nonhypertensive preterm births. The 3 risk factors with a differential impact, high maternal age, smoking, and maternal thinness, are well-proven correlates of intrauterine growth restriction.19 Thus SGA preterm birth appears to be influenced by risk factors specific to intrauterine growth restriction and also those related to preterm birth. In these analyses obstetric history, education, and marital status had a similar impact on the risk of preterm birth regardless of SGA status. Certain medical risk factors for preterm birth such as uterine malformations or placenta previa also had a similar effect on the risk of preterm birth for SGA and non-SGA fetuses. It appears that the predictors of SGA preterm birth are a combination of those that predict both preterm birth and SGA birth separately. Our findings are in line with other research on intrauterine growth restriction, in which preterm SGA births emerge as a distinct subset within the group of all SGA births with respect to some characteristics (such as maternal age and education) but not others (maternal height).20 Pregnancy-induced hypertension is also identified as a key correlate of SGA preterm birth in these studies.20, 21 Our results suggest that the effect of certain risk factors across populations in space and time could be influenced by rates of intrauterine growth restriction, hypertension, and induction. For instance, both maternal body mass index and smoking have an inconsistent relationship with preterm birth, which could be partially explained by these factors. Kramer et al22 did not find that maternal body mass index was a significant predictor of preterm birth, in contrast with our results and other studies that do show an impact.23 A Finnish study reported differences in the impact of low and high body mass index between a recent birth cohort (where they

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were significant risk factors) and a cohort of births from the 1960s (where they were not).24 Smoking has been found to be associated with preterm birth in some studies18 but not others.8-10 A better understanding of the specific risk factors for growth-restricted preterm births is important, especially given the poor prognosis of these infants compared with that of normally grown preterm infants.25 An approach that incorporates birth weight and other medical risk factors can also facilitate comparisons between studies and clarify the reasons underlying observed associations. We thank the members of the EUROPOP group (listed in the Appendix) and express our appreciation to the interviewers and technical teams in each participating country. We are also grateful for the computer and analysis assistance provided by Nathalie Lelong. REFERENCES

1. Savitz DA, Blackmore CA, Thorp JM. Epidemiologic characteristics of preterm delivery: etiologic heterogeneity. Am J Obstet Gynecol 1991;164:467-71. 2. Berkowitz GS, Blackmore-Prince C, Lapinski RH, Savitz DA. Risk factors for preterm birth subtypes. Epidemiology 1998; 9:279-85. 3. Goldenberg RL, Klebanoff MA, Nugent R, Krohn MA, Hillier S, Andrews WW. Bacterial colonization of the vagina during pregnancy in four ethnic groups. Vaginal Infections and Prematurity Study Group. Am J Obstet Gynecol 1996;174:1618-21. 4. Ott WJ. Intrauterine growth retardation and preterm delivery. Am J Obstet Gynecol 1993;168:1710-5. 5. Marsal K, Persson PH, Larsen T, Lilja H, Selbing A, Sultan B. Intrauterine growth curves based on ultrasonically estimated foetal weights. Acta Paediatr 1996;85:843-8. 6. Zeitlin J, Ancel P, Saurel-Cubizolles M, Papiernik E. The relationship between intrauterine growth restriction and preterm delivery: an empirical approach using data from the European Program on Occupational Risks and Pregnancy Outcome. Br J Obstet Gynaecol 2000;107:750-8. 7. Arbuckle TE, Sherman GJ. Comparison of the risk factors for pre-term delivery and intrauterine growth retardation. Paediatr Perinatal Epidemiol 1989;3:115-29. 8. Lang JM, Lieberman E, Cohen A. A comparison of risk factors for preterm labor and term small-for-gestational-age birth. Epidemiology 1996;7:369-76. 9. Abrams B, Newman V. Small-for-gestational-age birth: maternal predictors and comparison with risk factors of spontaneous preterm delivery in the same cohort. Am J Obstet Gynecol 1991; 164:785-90. 10. Wen SW, Goldenberg RL, Cutter GR, Hoffman HJ, Cliver SP. Intrauterine growth retardation and preterm delivery: prenatal risk factors in an indigent population. Am J Obstet Gynecol 1990;162:213-8. 11. Ancel P-Y, Saurel-Cubizolles M-J, DiRenzo GC, Papiernik E, Bréart G. Very and moderate preterm births: are the risk factors different? Br J Obstet Gynaecol 1999;106:1162-70. 12. Gardosi J, Mongelli M, Wilcox M, Chang A. An adjustable fetal weight standard. Ultrasound Obstet Gynecol 1995;6:168-74. 13. Hadlock FP, Harrist RB, Martinez-Poyer J. In utero analysis of fetal growth: a sonographic weight standard. Radiology 1991; 181:129-33. 14. Mongelli M, Gardosi J. Reduction of false-positive diagnosis of fetal growth restriction by application of fetal growth standards. Obstet Gynecol 1996;88:844-8. 15. Heinonen K, Matilainen R, Koski H, Launiala K. Intrauterine growth retardation (IUGR) in pre-term infants. J Perinat Med 1985;13:171-8. 16. Ananth CV, Berkowitz GS, Savitz DA, Lapinski RH. Placental abruption and adverse perinatal outcomes. JAMA 1999;282: 1646-51.

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17. Wolf EJ, Mallozzi A, Rodis JF, Egan JF, Vintzileos AM, Campbell WA. Placenta previa is not an independent risk factor for a small for gestational age infant. Obstet Gynecol 1991;77:707-9. 18. Kyrklund-Blomberg NB, Cnattingius S. Preterm birth and maternal smoking: risks related to gestational age and onset of delivery. Am J Obstet Gynecol 1998;179:1051-5. 19. Kramer M. Socioeconomic determinants of intrauterine growth retardation. Eur J Clin Nutr 1998;52(Supp 1):S29-S33. 20. 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;105:1011-7. 21. Kramer MS, Platt R, Yang H, McNamara H, Usher RH. Are all growth-restricted newborns created equal(ly)? Pediatrics 1999; 103:599-602. 22. Kramer MS, McLean FH, Eason EL, Usher RH. Maternal nutrition and spontaneous preterm birth. Am J Epidemiol 1992; 136:574-83. 23. Berkowitz GS, Papiernik E. Epidemiology of preterm birth. Epidemiol Rev 1993;15:414-43. 24. Olsén P, Läära E, Rantakallio P, Järvelin M-R, Sarpola A, Hartikainen A-L. Epidemiology of preterm delivery in two birth cohorts with an interval of 20 years. Am J Epidemiol 1995; 142:1184-93. 25. Piper JM, Xenakis EM, McFarland M, Elliott BD, Berkus MD, Langer O. Do growth-retarded premature infants have different rates of perinatal morbidity and mortality than appropriately grown premature infants? Obstet Gynecol 1996;87:169-74.

Appendix Composition of the EUROPOP group Members of the steering committee Project leader: Di Renzo GC, Perugia, Italy Bréart G, MD, Paris, France Papiernik E, MD, Paris, France Patel N, MD, Dundee, United Kingdom Saurel-Cubizolles MJ, PhD, Villejuif, France Taylor D, MD, Leicester, United Kingdom Todini S, MSc, Perugia, Italy Members of the National Staffs Czech Republic: Kudela M, MD, Vetr M, MD, in Olomouc Finland: Heikkilä A, MD, Erkkola R, MD, Forström J, MD, in Turku France: Papiernik E, MD, Lucidarme P, midwife, in Paris; Tafforeau J, MD, in Brussels Germany: Künzel W, MD, Herrero-Garcia J, MD, in Giessen; Dudenhausen J, MD, Henrich W, MD, in Berlin Greece: Antsaklis A, MD, Haritatos G , MD, in Athens Hungary: Kovacs L, MD, Nyari T, MD, Bartfai G, MD, in Szeged Ireland: O’Herlihy C, MD, Murphy J, MD, Stewart H, in Dublin Italy: Di Renzo GC, MD, Bruschettini PL, MD, Moscioni P, MD, in Perugia; Cosmi E, MD, Spinelli A, MD, Serena D, MD, in Rome Poland: Breborowicz GH, MD, Anholcer A, MD, in Poznan Romania: Stamatian F, MD, in Cluj Russia: Mikhailov AV, MD, in St. Petersburg Slovenia: Pajntar M, MD, Pirc M, MD, Verdenik I, MD, in Ljubljana Spain: Escribà-Aguir V, MD, in Valencia; Carrera JM, MD, in Barcelona Sweden: Marsal K, MD, Stale H, MD, in Malmö The Netherlands: Buitendijk S, PhD, van der Pal K, MsC, in Leiden; van Geijn H, MD, in Amsterdam Turkey: Gökmen O, MD, Güler C, MD, Caglar T, MD, in Ankara United Kingdom: Owen P, MD, in Dundee