IJE Advance Access published online on June 24, 2008
International Journal of Epidemiology, doi:10.1093/ije/dyn112
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Socioeconomic position and the risk of preterm birth—a study within the Danish National Birth Cohort
1 National Institute of Public Health, Copenhagen, Denmark.
2 Department of Biostatistics, University of Copenhagen, Denmark.
3 Epidemiology, Institute of Public Health, University of Southern Denmark, Denmark.
* Corresponding author. National Institute of Public Health, University of Southern Denmark, Oester Farimagsgade 5, DK-1399 Copenhagen K, Denmark. E-mail: csm{at}niph.dk
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Abstract |
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Background Low socioeconomic position is generally associated with increased risk of preterm birth, but it remains unclear whether the inequality depends on the socioeconomic measure used, if the associations differ according to the degree of prematurity, and how individual level risk factors mediate the association.
Methods The hazard ratios (HR) of preterm birth associated with five different measures of socioeconomic position and three degrees of preterm birth were analysed in a dataset of 75 890 singleton pregnancies (1996–2002) from the Danish National Birth Cohort. This, and the mediating role of selected individual level risk factors (smoking, alcohol consumption, binge drinking, pre-pregnancy body mass index, gestational weight gain) were estimated, using Cox regression analyses.
Results Mothers with <10 years of education had an elevated risk of preterm birth compared with mothers with >12 years of education and the association interacted with parity, while income and occupation affected the risk to a lesser degree. The adjusted HR for less educated nulliparous and parous women were 1.22 (95% CI 1.04–1.42) and 1.56 (95% CI 1.31–1.87), respectively, compared with women with >12 years of education. For parous women with <10 years of education inclusion of smoking in the model decreased the HR of preterm birth to 1.43 (95% CI 1.19–1.72).
Conclusions Maternal educational level was the strongest predictor of preterm birth among five socioeconomic measures and the gradient did not differ significantly according to the degree of preterm birth. For parous women smoking explained some of the educational gradient but in general the selected risk factors only reduced the relative educational gradient in preterm birth marginally.
Keywords Premature labour, gestational age, socioeconomic position, pregnancy, obstetrics
Accepted 24 April 2008
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Introduction |
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Preterm birth is associated with high risk of infant mortality and morbidity and has in most cases an unknown aetiology.1,2 The risk of preterm birth has been shown to be elevated among the socioeconomic disadvantaged women when measured by maternal educational level.3–10 Few studies have investigated more than one socioeconomic indicator3,7,11 as well as the socioeconomic position of the father.8 Heterogeneity of the association between different socioeconomic characteristics and preterm birth might shed light on the mechanisms behind a possible social gradient. Extremely preterm birth is associated with high risks of death, chronic lung disease,12 cerebral palsy,13 retinopathy of prematurity14 and other kinds of disabilities,15 while the moderately preterm born child faces a risk of disability that is almost similar to the child born term.16,17 Because of these profoundly different consequences according to the degree of prematurity, interventions aiming at reducing the risk of death and severe disability among the children born preterm should focus on reducing the number of clinically important cases.18 However, many studies have failed to report more than one degree of prematurity (<37 full weeks of gestation) when considering socioeconomic inequality in preterm birth. Previously it has been shown that people of disadvantaged socioeconomic position are more likely to live a sedentary lifestyle, to be overweight and to be smokers.19,20 Therefore, it has been hypothesized that socioeconomic disadvantage affects health through a higher prevalence of individual level risk factors among those with lower levels of education21 but it has to our knowledge not previously been quantified in the case of preterm birth.
In the present study, we analysed data from 75 980 women with a singleton pregnancy in Denmark between 1996 and 2002. The aim of this article was to clarify and compare how five different indicators of socioeconomic position were associated with preterm birth in order to create a better understanding for the mechanisms linking socioeconomic inequality with preterm birth. Further, the aim was to clarify whether the associations differed according to the degree of preterm birth and to explore to what extend five selected risk factors could explain some of the socioeconomic inequality in preterm birth. We hypothesized education, occupation and household income to represent different aspects of socioeconomic position22,23 and, therefore, to have different associations with preterm birth. We hypothesized smoking,16,24–27 alcohol consumption,28–31 pre-pregnancy body mass index (BMI)32 and gestational weight33 gain to be possible intermediate variables linking socioeconomic disadvantage with preterm birth. These indicators were chosen because of their possible association with socioeconomic position34–38 and preterm birth18,24,27–33 and because of their statistical association with preterm birth in our data material. Our findings may improve the understanding of the factors and processes that mediate the socioeconomic disparities in preterm birth and may create a basis for future interventions or studies with the purpose to reduce the socioeconomic inequality in preterm birth.
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Methods |
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This study was carried out within the Danish National Birth Cohort (DNBC), a nationwide ongoing study of pregnant women and their offspring. Between 1996 and 2002 women were invited to the cohort at their first antenatal visit at the general practitioner. It is estimated that about 30% of all Danish pregnant women in the years between 1996 and 2002 were recruited to the cohort. The women were included if they intended to carry the pregnancy to term, had a permanent address in Denmark and spoke Danish well enough to participate in telephone interviews. The women provided information on exposures during pregnancy by means of computer-assisted telephone interviews and the first interview was scheduled to take place in pregnancy week 12 (range 7–37). More details about the cohort are presented elsewhere.39–41
A total of 100 418 pregnant women were enrolled in the cohort. For this study, we initially included the 90 165 pregnancies for which we had a first pregnancy interview. Subsequently, we excluded 1985 women with multiple pregnancies, 918 women with a pregnancy terminated before 22 completed gestational weeks, 5428 women because of missing data on the covariates used in the analysis, 5833 pregnancies because the women participated in the cohort with more than one pregnancy (the first pregnancy was included), 21 women because the interview took place later than 37 weeks of gestation. Thus, 75 980 were eligible for analyses in this study. A total of 97.4% of the women were of Danish origin, 2% were from Scandinavian or other OECD countries and 0.5% from the rest of the world (0.1% unknown).
Measurement of outcome
The outcome measure of interest was gestational age at delivery. This information was based on information from the National Discharge Register. These estimates were predominantly based on ultrasound examination before 24 weeks of gestation, since all Danish women are invited for a scan at this stage. Preterm delivery was defined as birth after 22 completed gestational weeks (after 153 days) and before 37 completed gestational weeks (before 259 days) and was subdivided into three degrees of prematurity: extremely preterm (22–27 completed weeks), very preterm (28–31 completed weeks) and moderately preterm birth (32–36 completed weeks).
Measurement of exposure
Individual information of socioeconomic measures for each year was obtained from the Integrated Database for Labour Market Research in the year before the birth. The national educational codes were categorized according to the international ISCED classification system and were converted into three educational groups reflecting the highest number of years of completed academic educational attainment [9 years or less (pre-primary, primary and lower secondary), 10–12 years (upper secondary, post-secondary) and 13 years or more (tertiary)]. Occupation was categorized as: self-employed, employed (blue collar, lower and upper white collar workers), unemployed, student, disability-retired and unknown. Income was measured as the disposable household-size income the year before birth and calculated as the sum of the parents income adjusted for the number of adults and children in the household according to an OECD method.42 Income was grouped into six categories by percentiles: (i) <5 percentile, (ii) 5–25 percentile, (iii) >25–50 percentile, (iv) >50–75 percentile, (v) >75–95 percentile and (vi) >95 percentile based on the income of all Danish households with a child born between 1996 and 2002.
Measurement of potential confounders and effect modifiers
The covariates included were: maternal and paternal age (<25, 25–29, 30–34, 35–39, 
40), parity (0, 1+), fertility treatment (yes, no), bleeding during pregnancy (yes, no), maternal cohabitation (living alone, cohabitant), maternal height (
160 cm, >160 to <170 cm, 170 cm) and chronic disease (defined as having any of the following; high blood pressure, metabolic disorder, muscle and skeletal diseases, mental disorder, diseases in the urinary system, diseases in the abdominal region, urinary bladder infection more than five times or having had a cervical cone biopsy).
Potentially mediating individual level risk factors
The selected risk factors were cigarette smoking (not smoking, 0–10 g of tobacco/day, >10 g of tobacco/day), alcohol drinking (non-drinking, <1 unit of alcohol/week, 1–2 units/week, 3–5 units/week and >5 units/week), binge drinking (yes, no), BMI (<18.5, 18.5–24.99, 25–29.99, 30–34.99 and 35+ according to WHO guidelines43) and gestational weight gain (<0.2 kg/week, 0.2 to <0.5 kg/week and 0.5 kg/week).
Statistical analyses
The hazard ratios (HR) of preterm delivery according to five different measures of socioeconomic position were estimated using Cox regression. Gestational age in days was used as the underlying time. We used a model with delayed entry, so women entered the cohort at the day she completed 22 weeks of gestation (154 days) or on the day of her first pregnancy interview, whatever came last. The follow-up ended at birth, emigration, maternal death or by the time she completed 37 weeks of gestation (258 days), whichever came first. Deliveries that occurred after 258 days were censored at that time. We conducted five sets of analyses estimating the relations between maternal educational level, paternal educational level, maternal occupation, paternal occupation and household income, respectively, and preterm birth. Since parity modified the effect of maternal educational level and paternal occupation these analyses were conducted for nulliparous and parous separately. We estimated the influence of socioeconomic position on extremely, very and moderate preterm birth, respectively, by including an interaction term between the five indicators of socioeconomic position and time, defined as 22–27 completed weeks of gestation, 28–31 completed weeks of gestation and 32–36 completed weeks of gestation. Variables that changed the estimate (done by stepwise backwards elimination) by >5% were included in the analysis.44 We tested for interactions, which a priori were considered plausible, i.e. interactions between the five socioeconomic measures and cohabitation, fertility treatment, parity, paternal and maternal age, respectively.
The crude analyses were repeated in a subpopulation of women with no record of any chronic disease and in an unselected population of mothers and fathers of all Danish children born between 1996 and 2002. The HR of preterm birth were estimated for the group with unknown exposure level for each of the five socioeconomic indicators. The potential intermediate variables were tested for association with education on a 5% significance level by the 
2 test and the variables were tested for relation to preterm birth in Cox regression analyses. The mediating role of risk factors was assessed by comparing the adjusted estimates of the association between maternal educational level and preterm birth before and after including the variables in the Cox regression model. All statistical analyses were performed using the SAS software package version 9.1.
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Results |
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The mean gestational age was 279 days (range 154–308 days) and the overall proportion of preterm birth was 5.0% (3.797/7.980). Among the preterm births, the proportion of extremely preterm was 7.7%, the proportion of very preterm was 10.4% and the proportion of moderately preterm births was 81.9%. Based on the percentage wise distribution of characteristics of the mother and father in relation to preterm birth, it seemed that women with all types of preterm birth was more often lower educated, having the child with a man with a low educational level, living in a household with an income below the 25–50 percentile, in fertility treatment prior to the current pregnancy, smoking during pregnancy, cohabitant and bleeding during pregnancy prior to the interview. Furthermore, women with a preterm birth seemed to have a greater proportion of nulliparous and women with a chronic disease. Women with a preterm birth were not different from women with term births regarding maternal age, paternal age, maternal occupation, paternal occupation, maternal height, mean alcohol consumption, episodes with binge drinking during pregnancy, mean pre-pregnancy BMI and average weight gain in the first trimester (Tables 1 and 2).
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Associations between socioeconomic position and preterm birth
An educational level below 13 years was associated with an elevated HR of preterm birth. Additionally, a likelihood ratio test for trend revealed a significant trend in the results (P < 0.001). The gradients in HR of preterm birth differed with parity, such that and the educational gradient was steepest among women who had given birth before. There was a slightly elevated risk of preterm birth among couples where the father had a low educational level and a likelihood ratio test for trend showed that paternal education had an independent effect on preterm birth. Disability-retired mothers had an elevated HR of preterm birth compared with employed mothers. Income displayed a slight gradient in risk of preterm birth (linear variable in the model, P = 0.02), with a decreased risk of preterm birth for women who were living in low-income households (Table 3).
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Repeating the analyses in the subpopulation with no record of chronic disease (n = 57 883) and in the subpopulation of women without record of fertility treatment (n = 71 452) did not change the estimates (data not shown), except for the effect of disability retired women. The association between receiving disability pension and preterm birth was not present [the estimate was reduced from 2.24 (95% CI 1.50–3.35) to 0.37 (95% CI 0.05–2.66)] in this subpopulation.
Associations between socioeconomic position and extremely, very and moderately preterm birth
Results from the analyses with preterm birth grouped into extremely, very and moderately preterm birth, respectively, showed that the educational gradient was steeper among the group of women with very and extremely preterm births (Table 4). The results revealed no consistent patterns for differences in the gradients for occupation and household income and the test for proportional hazard showed no significant difference in the associations between the five measures of socioeconomic position, and extremely, very and moderately preterm birth, respectively (results not shown).
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The mediating role of health behaviours
Since maternal educational level was the clearest predictor of preterm birth among five socioeconomic measures, analyses regarding mediation were only made on the association between maternal education and the risk of preterm birth (Table 5). The overall reduction in the HR of preterm birth after including smoking, alcohol consumption, binge drinking pre-pregnancy BMI and gestational weight gain in the model was higher among parous than among nulliparous women. Including all five mediators lowered the risk estimate by 19% [(HRwithout the mediator in the model–HRwith the mediator in the model)/HRwithout the mediator in the model–1]45 for nulliparous with the lowest educational level and 30% for parous women with the lowest educational level. The elevated risk of preterm birth among nulliparous women was reduced only marginally when the intermediate variables were included in the regression model separately, but smoking reduced the HR of preterm birth with 23% for parous and low educated (<10 years) women and with 13% for parous women with 10–12 years of education.
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Discussion |
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This study, using birth outcomes from 75 980 pregnancies, showed that maternal educational level was the indicator of socioeconomic position that most clearly displayed a social gradient in preterm birth and that the gradient was steepest among parous women. The trend test results displayed a slightly increased risk of preterm birth if the father of the child had a low educational level and a slightly decreased risk if the household income was low. There was no statistically significant difference in the associations according to the degree of preterm birth. Smoking explained some of the relative educational gradient in preterm birth among parous women and a minor part of the association for nulliparous women. Alcohol consumption, binge drinking, pre-pregnancy BMI and gestational weight gain explained only a minor part of the association between maternal educational level and preterm birth and only for women with an educational attainment below 10 years.
Previous studies based on populations from Europe,46 Norway,9 Sweden,4,5 Finland8 and Canada6 have found strong evidence for socioeconomic differences in preterm birth, when measured by maternal educational level. Other studies reported a greater elevated HR of preterm birth among women with the lowest educational level than found in the present study, which may be explained by a higher level of education in the reference group and different degrees of socioeconomic inequalities in different countries. Two of the previous studies investigating the difference in associations between maternal educational level and preterm birth according to the degree of preterm birth (<32 weeks and below 37 weeks) likewise showed a steeper gradient in very preterm birth than in moderately preterm birth.3,6 In one study, the authors found an inverse association but the outcome in this study was small for gestational age (SGA) children born preterm, which can not be compared directly with preterm births because the aetiology of SGA might differ from that of preterm birth.10 In a study based on a Dutch population, the authors did not find any association between maternal educational level and preterm birth but the estimates showed a tendency to a gradient and the insignificant results could be due to a small sample size (2027 women and 108 preterm births).
The results of no association between occupation and preterm birth is in agreement with the earlier findings from Denmark47 and from Finland,8,48 but in a study by Ancel et al.46 with data from all over Europe, unemployed women had an elevated risk of preterm birth compared with employed women. The difference could be due to a modifying effect from the welfare models in the Nordic countries with high social security for people outside the labour market. The elevated risk of preterm birth among disability-retired women was not surprising since the illness (for instance chronic diseases) that entitled the woman to the National Supplementary Disability Pension could be related to the risk of preterm birth. The additional analyses on a population with no record of chronic disease showed a much lowered HR of preterm birth for the disability retired women [the estimate was reduced from 2.24 (95% CI 1.50–3.35) to 0.37 (95% CI 0.05–2.66)] indicating that it was the disease rather than the attachment to the labour market that contributed to the elevated HR of preterm birth for the disability-retired women.
The results on the tendency to a reduced HR of preterm birth for women living in low-income households are surprising. The CIs did all include 1 and the trend test was not statistically significant, but as the model with a linear variable for income described the data best and in this model, there was some statistical support for positive trend in the association between income and preterm birth i.e. lower income was associated with lower risk of preterm birth, once adjustment was made for the other socioeconomic indicators. From the results, it is not possible to conclude that a low income has a protective effect, however, lower household income does not seem to be a risk factor for preterm birth in this study. The association between income at individual level and preterm birth is to our knowledge not previously investigated. A study by Gudmundsson et al.49 reported no elevated risk for women from deprived neighbourhoods and Luo et al.7 reported a slightly elevated risk for woman from areas with income in the lowest quintile. This is contrary to the findings in this study but these studies were not directly comparable with this study because of the area-based exposure measure.50
The literature on the role of individual level risk factors as mediating factors explaining socioeconomic inequality in preterm birth is sparse. Kramer et al.35 have addressed the contribution of individual level risk factors to socioeconomic inequality in preterm birth. In this study, the authors suggested bacterial vaginosis and cigarette smoking were the quantitatively most important mediators on the association between socioeconomic position and preterm birth. We had no good clinical information regarding bacterial vaginosis and urinary tract infection, which is a limitation in this study, as these can display a social gradient and explain a proportion of the socioeconomic inequality found in preterm birth. Smoking was the most important mediator in this study; we need, however, access to a wider array of potentially mediating variables in order to conclude that it is the most important. With regards to birth weight, smoking has been shown to mediate the association between social deprivation and pre- and full-term low birth weight.51 In a study by Stephansson et al.,52 the relative increased risk of stillbirth among women with low socioeconomic status could not be explained by differences in lifestyle factors such as smoking and BMI, which support the findings of the present study although the results are not directly comparable.
This study was based on a large population with complete follow-up. The proportion with missing information was relatively low. A possible limitation of this study is the selection to the DNBC, which could introduce selection bias. In order to examine the magnitude of selection bias, we repeated the unadjusted analysis in the source population consisting of 506 598 (all) Danish births between 1996 and 2002. The results showed a greater elevated risk of preterm birth among the socioeconomic disadvantaged in the general Danish population but the relative HR (Relative HR = HR in DNBC/HR in the Danish population) differed only between 4% and 12% from the unadjusted results presented in this study (results not shown). We believe the selection to the cohort to have affected the internal validity only slightly, which is supported by results from a Danish study from 2006,53 but it may reduce the generalizability of the study.
The group of women and men with no information on education or occupation and births with unknown father had a high relative elevated risk of preterm birth compared with the reference groups (>12 years of education, employed, income in the 50–75 percentile, data not shown) but including these groups in the analyses did only change the estimates for the association between paternal educational level and preterm birth (upwards with 9%). The interpretation of the elevated risks is unclear, since unknown status of the father may both represent unwanted pregnancies and pregnancies where the women is no longer in relationship with the infants father, in addition to lack of registration if the infant died shortly after the birth.
The bias due to possible self-under or over reporting of socioeconomic position is minimized in this study, since exposure was registry-based information. However, information on covariates and health behaviours such as fertility treatment, bleeding during pregnancy, chronic disease, smoking, drinking etc. was obtained by self-reports. Literature on the accuracy on self-reported health behaviours suggest that, although most people report honestly, the respondents tend to underreport characteristics that are considered to be undesirable or negative54 and this could be more pronounced among women with a low educational level.55 If underreporting of smoking is more common among the women with a low educational level, the proportion of socioeconomic inequality in preterm birth explained by smoking would be underestimated. The method used to investigate the mediating role of individual level risk factors has its limitations, since this proportion is dependent on the choice of reference group for the exposure variables. Thus, the calculated percent-wise change in the estimates should be interpreted with caution. We did repeat the analyses using other reference groups and this revealed the same tendencies as shown in this article.
The educational gradient in preterm birth might be explained by different use of the health care system, even though the access to care is free and in principle equal in Denmark, if well-educated women optimise their use of the health services through better communication with health professionals. However, the evidence of the effect of prenatal care on preterm birth is diverse.56–59 Furthermore, education can be regarded as marker of a life long exposure because education reflects a number of possible key exposures—among other things—the childhood intellectual and material resources, and education may in this way be a more precise indicator of socioeconomic conditions across the life course.
The slightly positive income gradient in preterm birth could indicate that material wealth does not have a substantial influence on preterm birth or that a possible inverse income gradient is modified by the welfare system.
In conclusion, maternal educational level was the strongest predictor of preterm birth among five socioeconomic measures. Income displayed a slightly positive gradient indicating that women from low-income household had a decreased risk of preterm birth. The educational gradient was steepest among the more ‘severe’ types of preterm birth, but the differences were not statistically significant. Smoking was the only factor that contributed to explaining the educational gradient in preterm birth and this was only persistent among parous women.
KEY MESSAGES
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The Danish National Research Foundation has established the Danish Epidemiology Science Centre that initiated and created the Danish National Birth Cohort. The cohort is furthermore a result of a major grant from this Foundation. Additional support for the Danish National Birth Cohort is obtained from the Pharmacy Foundation, the Egmont Foundation, the March of Dimes Birth Defects Foundation, the Augustinus Foundation and the Health Foundation. The authors wish to thank Sarah Fredsted Villadsen for valuable comments to this article.
Conflict of interest: None declared.
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