Conceived and designed the experiments: DT-R ZA. Performed the experiments: DT-R. Analyzed the data: DT-R MJP ZA BY PD. Contributed reagents/materials/analysis tools: DT-R UA MJP BY ZA. Wrote the paper: DT-R ZA.
The authors have declared that no competing interests exist.
Social deprivation is associated with higher rates of preterm birth and subsequent infant mortality. Our objective was to identify risk factors for preterm birth in the UK's largest maternity unit, with a particular focus on social deprivation, and related factors.
Retrospective cohort study of 39,873 women in Liverpool, UK, from 2002–2008. Singleton pregnancies were stratified into uncomplicated low risk pregnancies and a high risk group complicated by medical problems. Multiple logistic regression, and generalized additive models were used to explore the effect of covariates including area deprivation, smoking status, BMI, parity and ethnicity on the risk of preterm birth (34+0 weeks). In the low risk group, preterm birth rates increased with deprivation, reaching 1.6% (CI95 1.4 to 1.8) in the most deprived quintile; the unadjusted odds ratio comparing an individual in the most deprived quintile, to one in the least deprived quintile was 1.5 (CI95 1.2 to 1.9). Being underweight and smoking were both independently associated with preterm birth in the low risk group, and adjusting for these factors explained the association between deprivation and preterm birth. Preterm birth was five times more likely in the high risk group (RR 4.8 CI95 4.3 to 5.4), and there was no significant relationship with deprivation.
Deprivation has significant impact on preterm birth rates in low risk women. The relationship between low socio-economic status and preterm births appears to be related to low maternal weight and smoking in more deprived groups.
Preterm birth is an important public health issue in the UK and internationally, since prematurity is a major contributor to infant mortality and inequalities in health
There is increasing interest in the UK in publishing and comparing clinical outcomes across centres
Since the aetiology of preterm birth is significantly different in multiple pregnancies, women with a previous history of preterm birth, and following intrauterine transfers to tertiary obstetric units, we aimed to explore the effect of SES in singleton, booked pregnancies regarded clinically as either high or low risk. Our a priori hypothesis was that there would be differential rates of preterm birth by socioeconomic status, and that these might be related to individual level risk factors. We show that deprivation is indeed an important risk factor in low risk pregnancies, and that this is related to maternal smoking and underweight.
Ethical approval for this study was sought and granted by the Sefton Research Ethics Committee. We used routinely collected data from electronic hospital records, analysed anonymously, and individual patient consent was not required.
To quantify the effect of social deprivation and other risk factors on preterm delivery in high and low risk pregnancies.
We undertook a retrospective cohort study using routinely collected data from the Liverpool Women's NHS Foundation Trust (LWH). This is the single largest maternity unit in the UK, delivering around 8,000 babies and caring for around 1,000 preterm infants in the neonatal unit annually. We accessed data from the LWH Meditech hospital information system on all women delivering after 24+0 weeks gestation over a seven year period from 2002–2008. In order to avoid clustering of risk factors, subsequent pregnancies of women who had more than one pregnancy during the data collection period were excluded from the analysis. The data extracted contained detailed information on demographics, previous and current obstetric history and details of medical conditions.
We excluded intrauterine transfers (IUTs), multiple pregnancies, and pregnancies in women with a previous history of preterm delivery <34 weeks from this analysis. The remaining pregnancies were allocated to the high-risk group if they had significant medical conditions. Two obstetricians reviewed all the coded data on co-morbidity in pregnancy, and identified all medical disorders of potential relevance to preterm birth. These included problems identified at booking (e.g. cardiac disease, essential hypertension, epilepsy, diabetes, renal disease, SLE, thyroid disease, Crohn's disease, uterine abnormalities) and problems developed during pregnancy (e.g. gestational hypertension, pre-eclampsia/eclampsia, cholestasis, second trimester vaginal bleeding and Rhesus disease). The group remaining was our low risk population of interest, and represents uncomplicated singleton pregnancies with no identifiable major clinical risk factors for preterm birth.
The primary outcome was preterm birth before 34+0 gestational weeks (<238 days: PTB<34) calculated on the basis of first trimester scan. We included both spontaneous and obstetrically induced births less than 34 weeks gestation in this outcome as this group is likely to have significant morbidity, both short and long term, with important resource implications for health services
We aimed to explore the following covariates: maternal age, parity (nulliparous or not), smoking status (never, previous, current smoker<10 cigarrettes per day (cpd), and current smoker>10 cpd, and ), BMI at booking (<18.5 underweight, 18.5 to <35 reference, >35 obese) and ethnicity (self-reported categories coded to white and other). BMI was consistently collected from 2004 onwards in MEDITECH. Before 2004. BMI was calculated from height and weight where available. Postcodes were used to derive Index of Multiple Deprivation (IMD) scores for all of the pregnancies. The IMD combines a number of indicators, chosen to cover a range of economic, social and housing issues, into a single deprivation score for small areas in the UK
Although IMD is measured on a continuous scale, for descriptive summaries, we have followed the common practice of grouping IMD into quintiles. However, reducing IMD to a categorical variable looses information. For formal analysis of the association between deprivation and pre-term birth we therefore retained IMD as a continuous variable. G
51,857 pregnancies were recorded in the Meditech system. We excluded 431 intrauterine transfers, 940 multiple pregnancies and 732 pregnancies in women with a history of previous preterm birth, leaving 39,404 low risk pregnancies, and 10,351 high risk pregnancies. Selecting the first pregnancy for each woman during the data collection period resulted in the final sample of 31,785 low risk and 8,130 high-risk pregnancies. A valid postcode was available on all but 42 women leaving 39,873 pregnancies in the final analysis.
Overall, 2.6% (n = 1,036) of pregnancies resulted in preterm birth before 34 weeks, significantly more than 1.9% nationally in 2005
There were 1148 (3.6%) late preterm births in the low risk group, and 871 (10.7%) in the high risk group, equating to a late preterm birth relative risk of 2.97 (CI95 2.7 to 3.2) comparing the high risk group to the low risk group.
LOW RISK Deprivation quintile | 1 | 2 | 3 | 4 | 5 | All | p for trend |
N | 655 | 1543 | 4169 | 4930 | 20469 | 31766 | |
age<18 | 1 (0.2) | 7 (0.5) | 17 (0.4) | 36 (0.7) | 358 (1.7) | 419 (1.3) | 0.220 |
age>30 | 526 (80.3) | 1186 (76.9) | 2880 (69.1) | 2957 (60) | 7871 (38.5) | 15420 (48.5) | 0.000 |
White | 604 (92.2) | 1438 (93.2) | 3788 (90.9) | 4369 (88.6) | 17322 (84.6) | 27521 (86.6) | 0.000 |
Underweight | 4 (0.7) | 20 (1.6) | 82 (2.3) | 72 (1.7) | 600 (3.5) | 778 (2.9) | 0.000 |
Obese | 61 (11) | 149 (11.8) | 440 (12.6) | 574 (13.8) | 2939 (16.9) | 4163 (15.5) | 0.000 |
Smoker | 37 (5.6) | 122 (7.9) | 460 (11) | 794 (16.1) | 7009 (34.2) | 8422 (26.5) | 0.000 |
Smoker<10 | 32 (4.9) | 101 (6.6) | 380 (9.2) | 613 (12.5) | 5256 (25.8) | 6382 (20.2) | 0.000 |
Smoker>10 | 5 (0.8) | 21 (1.4) | 80 (1.9) | 181 (3.7) | 1753 (8.6) | 2040 (6.5) | 0.000 |
Previous smoker | 50 (7.7) | 139 (9.1) | 460 (11.1) | 545 (11.1) | 2272 (11.2) | 3466 (11) | 0.005 |
Nulliparous | 350 (53.7) | 859 (55.9) | 2443 (58.8) | 2956 (60.3) | 11659 (57.2) | 18267 (57.7) | 0.817 |
Caesarean section | 162 (24.7) | 379 (24.6) | 991 (23.8) | 1110 (22.5) | 3939 (19.2) | 6581 (20.7) | 0.000 |
Preterm<34 | 10 (1.5) | 15 (1) | 47 (1.1) | 61 (1.2) | 331 (1.6) | 464 (1.5) | 0.006 |
Preterm 34 to 37 | 17 (2.6) | 30 (1.9) | 129 (3.1) | 157 (3.2) | 815 (4) | 1148 (3.6) | 0.000 |
Preterm<37 | 27 (4.1) | 45 (2.9) | 176 (4.2) | 218 (4.4) | 1146 (5.6) | 1612 (5.1) | 0.000 |
Deaths in preterm<34 | 0 | 0 | 3 (8.5) | 5 (8.2) | 16 (4.8) | 24 (5.2) | 0.836 |
HIGH RISK Deprivation quintile | 1 | 2 | 3 | 4 | 5 | All | p for trend |
N | 216 | 449 | 1082 | 1242 | 5118 | 8107 | |
age<18 | 3 (1.4) | 0 (0) | 5 (0.5) | 7 (0.6) | 48 (0.9) | 63 (0.8) | 0.082 |
age>30 | 172 (79.6) | 364 (81.1) | 809 (74.8) | 843 (67.9) | 2369 (46.3) | 4557 (56.2) | 0.000 |
White | 201 (93.1) | 420 (93.5) | 1004 (92.8) | 1109 (89.3) | 4489 (87.7) | 7223 (89.1) | 0.000 |
Underweight | 1 (0.6) | 5 (1.4) | 27 (3) | 24 (2.3) | 171 (4) | 228 (3.4) | 0.000 |
Obese | 38 (22.4) | 54 (15.3) | 183 (20.4) | 245 (23.3) | 992 (23) | 1512 (22.3) | 0.001 |
Smoker | 12 (5.6) | 36 (8) | 124 (11.5) | 220 (17.7) | 1778 (34.7) | 2170 (26.8) | 0.000 |
Smoker<10 | 8 (3.7) | 32 (7.3) | 100 (9.3) | 166 (13.5) | 1253 (24.6) | 1559 (19.4) | 0.000 |
Smoker>10 | 4 (1.9) | 4 (0.9) | 24 (2.2) | 54 (4.4) | 525 (10.3) | 611 (7.6) | 0.000 |
Previous smoker | 16 (7.5) | 33 (7.5) | 93 (8.7) | 116 (9.4) | 526 (10.3) | 784 (9.7) | 0.006 |
Nulliparous | 109 (50.7) | 237 (53.3) | 607 (56.4) | 685 (55.5) | 2607 (51.1) | 4245 (52.6) | 0.028 |
Caesarean section | 85 (39.4) | 168 (37.4) | 370 (34.2) | 411 (33.1) | 1442 (28.2) | 2476 (30.5) | 0.000 |
Preterm<34 | 22 (10.2) | 35 (7.8) | 67 (6.2) | 73 (5.9) | 375 (7.3) | 572 (7.1) | 0.881 |
Preterm 34 to 37 | 21 (9.7) | 51 (11.4) | 115 (10.6) | 107 (8.6) | 577 (11.3) | 871 (10.7) | 0.320 |
Preterm<37 | 43 (19.9) | 86 (19.2) | 182 (16.8) | 180 (14.5) | 952 (18.6) | 1443 (17.8) | 0.481 |
Deaths in preterm<34 | 2 (9.1) | 3 (8.6) | 3 (4.5) | 6 (8.2) | 15 (4) | 29 (5) | 0.141 |
LOW RISK | ||||||||
PTB 34 | PTB 34–37 | |||||||
OR | 2.50% | 97.50% | p | OR | 2.50% | 97.50% | p | |
Most deprived | 1.47 | 1.16 | 1.86 | 0.00 | 1.56 | 1.34 | 1.82 | 0.00 |
age<18 | 1.97 | 1.04 | 3.38 | 0.02 | 1.12 | 0.66 | 1.77 | 0.66 |
age>35 | 0.91 | 0.73 | 1.14 | 0.44 | 0.96 | 0.83 | 1.11 | 0.61 |
Underweight | 2.34 | 1.47 | 3.55 | 0.00 | 1.43 | 1.01 | 1.96 | 0.03 |
Obese | 0.90 | 0.65 | 1.22 | 0.52 | 0.97 | 0.81 | 1.16 | 0.75 |
smoker<10 | 1.45 | 1.16 | 1.80 | 0.00 | 1.57 | 1.37 | 1.81 | 0.00 |
smoker>10 | 1.44 | 1.01 | 2.01 | 0.04 | 2.00 | 1.63 | 2.44 | 0.00 |
smoker previous | 0.89 | 0.62 | 1.22 | 0.48 | 1.05 | 0.85 | 1.28 | 0.64 |
white | 0.97 | 0.75 | 1.27 | 0.80 | 1.17 | 0.98 | 1.41 | 0.10 |
HIGH RISK | ||||||||
PTB 34 | PTB 34–37 | |||||||
OR | 2.50% | 97.50% | p | OR | 2.50% | 97.50% | p | |
Most deprived | 1.07 | 0.86 | 1.33 | 0.53 | 1.26 | 1.05 | 1.51 | 0.01 |
age<18 | 0.88 | 0.27 | 2.16 | 0.81 | 0.90 | 0.34 | 1.92 | 0.80 |
age>35 | 1.07 | 0.89 | 1.28 | 0.47 | 1.08 | 0.93 | 1.26 | 0.31 |
Underweight | 1.28 | 0.75 | 2.04 | 0.33 | 1.69 | 1.16 | 2.40 | 0.00 |
Obese | 0.84 | 0.65 | 1.08 | 0.18 | 1.03 | 0.85 | 1.24 | 0.78 |
smoker<10 | 0.85 | 0.67 | 1.06 | 0.15 | 1.40 | 1.17 | 1.66 | 0.00 |
smoker>10 | 1.27 | 0.94 | 1.68 | 0.11 | 1.64 | 1.29 | 2.08 | 0.00 |
smoker previous | 0.64 | 0.45 | 0.89 | 0.01 | 0.97 | 0.74 | 1.24 | 0.79 |
white | 0.94 | 0.72 | 1.23 | 0.62 | 0.78 | 0.63 | 0.96 | 0.02 |
For low risk late preterm births there was a similar relationship with deprivation, smoking and being underweight, but age<18 was not significant. For high risk late preterm births the most deprived quintile had an increased risk of preterm birth (OR 1.26 CI95 1.05 to 1.51), as did smokers, being underweight, and non-white women.
LOW RISK | PTB34 | PTB34 to 37 | ||||||
OR | 2.50% | 97.50% | p | OR | 2.50% | 97.50% | p | |
most deprived unadjusted | 1.47 | 1.16 | 1.86 | 0.00 | 1.56 | 1.34 | 1.82 | 0.00 |
most deprived adjusted | 1.25 | 0.90 | 1.73 | 0.16 | 1.32 | 1.12 | 1.64 | 0.00 |
smoker<10 | 1.82 | 1.40 | 2.36 | 0.00 | 1.50 | 1.28 | 1.76 | 0.00 |
smoker>10 | 1.93 | 1.30 | 2.78 | 0.00 | 1.79 | 1.42 | 2.23 | 0.00 |
smoker - previous | 1.15 | 0.79 | 1.65 | 0.44 | 1.03 | 0.83 | 1.28 | 0.76 |
Underweight | 2.11 | 1.31 | 3.21 | 0.00 | 1.28 | 0.90 | 1.77 | 0.15 |
Obese | 0.91 | 0.65 | 1.23 | 0.55 | 0.95 | 0.79 | 1.14 | 0.61 |
age<18 | 1.82 | 0.86 | 3.38 | 0.08 | 0.87 | 0.46 | 1.49 | 0.63 |
age>35 | 1.11 | 0.84 | 1.45 | 0.45 | 1.06 | 0.90 | 1.25 | 0.47 |
HIGH RISK | PTB34 | PTB34 to 37 | ||||||
OR | 2.50% | 97.50% | p | OR | 2.50% | 97.50% | p | |
most deprived unadjusted | 1.07 | 0.86 | 1.33 | 0.53 | 1.26 | 1.05 | 1.51 | 0.01 |
most deprived adjusted | 1.23 | 0.93 | 1.61 | 0.14 | 1.00 | 0.83 | 1.28 | 0.73 |
smoker<10 | 0.98 | 0.75 | 1.27 | 0.88 | 1.37 | 1.12 | 1.67 | 0.00 |
smoker>10 | 1.55 | 1.10 | 2.14 | 0.01 | 1.84 | 1.41 | 2.39 | 0.00 |
smoker - previous | 0.86 | 0.59 | 1.21 | 0.40 | 1.02 | 0.77 | 1.34 | 0.86 |
Underweight | 1.27 | 0.74 | 2.03 | 0.35 | 1.64 | 1.12 | 2.34 | 0.01 |
Obese | 0.84 | 0.65 | 1.08 | 0.19 | 1.06 | 0.88 | 1.28 | 0.53 |
age<18 | 0.29 | 0.02 | 1.32 | 0.22 | 0.85 | 0.29 | 1.96 | 0.74 |
age>35 | 1.02 | 0.81 | 1.28 | 0.84 | 1.16 | 0.97 | 1.38 | 0.10 |
Using routinely collected obstetric data from a retrospective cohort of 39,873 women with a singleton pregnancy, we were able to define a high risk group with a five fold greater risk of preterm birth before 34 weeks compared with the women without obvious risk factors. In otherwise low risk pregnant women deprivation, age <18, underweight and smoking were associated with preterm birth<34.
Our data, therefore, suggest that reducing the burden of disease due to very preterm birth will have to involve targeted, disease specific preventative interventions in high risk pregnancies, but for low risk groups, population level public health action is needed to address risk factors for preterm birth associated with social deprivation.
In women who gave birth between 34 and 37 weeks the distinction between low and high groups was less distinct – smoking was important, but age and obesity were not. Interestingly, being underweight was significant in high risk, but not in low risk. The opposite was found in very preterm births. The reasons for this pattern are unclear. Despite the large number of pregnancies analyzed, it is likely that our analysis was underpowered to detect a statistically significant association with being underweight in some of our subgroups. All of the point estimates for underweight are in the same direction, however, suggesting that underweight women are at increased risk of preterm birth.
We have demonstrated that it is possible to identify distinct clinically important groups, with markedly different preterm birth rates, on the basis of medical information collected during pregnancy. In-utero transfers, multiple pregnancies and women with a history of previous preterm birth have very different risks of preterm birth (results not reported here) and should be analysed separately. We suggest that stratification of birth outcomes in terms of these groups provides a more meaningful method to report preterm birth outcomes from maternity units for auditing and benchmarking purposes. So for example, rates of preterm birth in multiple pregnancies would provide an additional useful metric.
Reducing infant mortality, which is accounted for to a great extent by preterm birth, is a key focus of both the recent NHS white paper, and the Public Health white paper
It has been hypothesised that the relationship between low socioeconomic status and preterm birth may be explained by the clustering of demographic and ‘lifestyle’ risk factors in women from more disadvantaged backgrounds
In our cohort, being underweight (BMI<18.5) emerged as a clear risk factor in the low risk group, approximately doubling the risk of preterm birth<34. This is in line with a recent meta-analysis
A strength of our analysis is that we have a large sample, with individual level clinical data, and were able to stratify pregnancies in terms of major obstetric risk factors. The main purpose of this stratification was to allow a ‘cleaner’ view of the relationship between SES and preterm births, without the risk of confounding, particularly by medical problems. For this reason, we stratified the pregnancies based on a combination of characteristics that may or may not be present in early pregnancy. A different analytical approach would be required to quantify the risk of preterm birth for counselling purposes in early pregnancy. These methods and our findings are likely to be generalizable to other large tertiary maternity units in the UK, serving deprived populations.
This study is hospital based, rather than population based, but our cohort is likely to be similar to a population based cohort, since LWH is the main maternity unit in the Liverpool area, and there are no private providers. We have limited selection bias by excluding transfers in to LWH, who have a different risk profile. We had to rely on retrospective, routinely collected data, and there is scope for response bias in the self-reported smoking status covariate. We have used a standard small area based measure of deprivation. It was therefore not possible to separate effects of SES operating at the individual and area level, which may be distinct, as suggested in other studies of preterm births
Our results have a number of implications. Social deprivation is an important risk factor for preterm birth, and the effect of deprivation is related to maternal smoking and underweight, providing clear targets for public health action to reduce inequalities in preterm birth and subsequent infant mortality. At the individual level smoking is recognised as one of the most important “lifestyle” factors in the pathway to health inequalities, and persistent social gradients remain in the UK
The social distribution of preterm birth suggests that social factors – the “social determinants of health” – are having an important effect on outcomes. These are the “conditions in which we are born, grow up, work and live”
Further attempts to explore the pathways from low SES to preterm births need to take into account individual and area level mediators to identify further targets for preventative interventions. As a way forward, maternity units should produce preterm birth rates stratified by clinical risk factors and deprivation quintiles. Given the influence of SES, the proportion of preterm and term births to women in the most deprived normative English quintile should be used as a metric in comparing quality of maternity care across centers. Targeted analysis of outliers will provide important insights into possible preventative interventions and resource allocation by newly established GP consortia in the UK
Sandra Yahanee for assistance with data extraction.