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Journal of Public Health Advance Access originally published online on January 13, 2009
Journal of Public Health 2009 31(1):88-94; doi:10.1093/pubmed/fdn112
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© The Author 2009, Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved

Rising rates of obstetric interventions: exploring the determinants of induction of labour



T. Humphrey, Consultant Midwife

J. S. Tucker, Senior Researcher
Department of Obstetrics and Gynaecology, Dugald Baird Centre for Research on Women's Health, University of Aberdeen, Aberdeen, UK

Address correspondence to T Humphrey, E-mail: t.humphrey{at}abdn.ac.uk

Background Rising rates of obstetric interventions in the UK are a concern for health-care providers and the public. Our aims were to identify the socio-demographic and clinical factors (case mix) predictive of one of the most common obstetric interventions, induction of labour (IOL), and quantify the extent to which observed rates can be explained by case mix factors.

Methods We conducted a comparative analysis of induced and spontaneous labours, using contemporary clinical data from the Aberdeen Maternity and Neonatal Databank. Cases complicated by antenatal intrauterine death or a previous or planned caesarean section were excluded. In total, 17 736 cases were included in the analysis.

Results In 5727 (32.3%) cases labour was induced and in 12 009 (67.7%) cases it was spontaneous. Multivariate logistic regression modelling was used. In total, 18 case mix factors were predictive of IOL. Among these were well-recognized clinical indications for IOL such as pre-labour rupture of membranes (OR 3.29, 95% CI 2.90, 3.73) and prolonged pregnancy (OR 4.15, 95% CI 3.82, 4.50) and previously unreported case mix factors (residing an intermediate distance and travel time from hospital) (OR 1.27, 95% CI 1.18, 1.37; BMI >35 OR 1.37, 95% CI 1.14, 1.65). Case mix explained 71.5% of the observed rate of IOL.

Conclusions More than one-quarter of the rate of IOL remains unexplained by case mix factors. This may be explained by women's preferences for care and clinicians' practice.

Keywords: models, public health, research


   Introduction
TOP
 Introduction
Methods
 Results
 Discussion
 Conclusions
 Funding
 Acknowledgements
 References
 
Induction of labour (IOL) is defined as the artificial initiation of uterine contractions leading to dilation of the cervix ≥24 weeks gestation, in the presence or absence of ruptured membranes.1 It is now one of the most commonly performed procedures in obstetric practice. Considerable variation exists in reported rates of IOL between countries. In 2002, the USA was reported to have an IOL rate of 20%, while Scotland's was 27%.2,3 This variation in rates of IOL also occurs between hospitals. In 2005, IOL rates ranged from 19% to 40% in consultant units in Scotland.3 Similar to the well-reported debate about increasing caesarean section (CS) rates,4,5 since the mid-1980s, increasing rates of IOL have been reported in England, USA and Australia.1,3,6 In Scotland, the rates of IOL have risen from 20% in 1989 to 27% in 2003.3 This means that as many as one in four women are having their labour induced. Implicit in much of the literature on the rising trends of obstetric interventions are concerns about the lack of attributable health benefits for the public, concerns about potential harm and the increasing costs for the National Health Services (NHS).4,7 Recent government policy and initiatives around childbirth, focusing on promoting normality and reducing unnecessary intervention remain unevaluated.8–10

Although there is some consistency in the evidence about the benefits of IOL in prolonged pregnancy,11 pre-labour rupture of membranes12 (PROM) and diabetes mellitus,13 some observational studies indicate that IOL for other indications may be associated with poorer outcomes in women and babies.14,15 National clinical guidelines in the UK recommend that IOL is only indicated when it is likely that a better outcome will result if labour is initiated, than if the pregnancy continues.1 This would suggest that the majority of the observed rates of IOL in maternity units in the UK should be explained by the clinical characteristics of women presenting for obstetric care. This study aims to identify the case mix factors predictive of IOL compared with spontaneous labour, and to quantify the extent to which case mix explains the observed rate of IOL.


   Methods
TOP
 Introduction
 Methods
Results
 Discussion
 Conclusions
 Funding
 Acknowledgements
 References
 
The tertiary maternity hospital for the NHS North of Scotland region has almost 5000 births per annum. It is a university teaching hospital that offers midwifery-led care for healthy women with uncomplicated pregnancies, and provides specialist obstetric, anaesthetic and neonatal services for complex cases. Like other tertiary hospitals in the UK, it is located in a densely populated area and serves a large catchment area. Since 1951, the Aberdeen Maternity and Neonatal Databank (AMND), an electronic databank, has routinely collected and stored detailed information of each pregnancy and birth at Aberdeen Maternity Hospital (AMH). Coders trained by clinicians extract and code data from clinical records using International Classification of Diseases.16 These data entry, storage and retrieval methods are subjected to regular internal validation checks, including verification by a clinical epidemiologist, checks for valid ranges of measurable variables and date checks for both ranges and validity. Previous studies using data from the AMND cite an inconsistency rate of 0.014%.17 AMND data include social, demographic and clinical data about childbearing women and neonates.

Anonymized data were extracted from the AMND for a contemporary consecutive series of births between 1999 and 2003 (inclusive). Inclusion criteria were singleton pregnancies that delivered ≥24 weeks gestation with a live foetus at the onset of labour. Exclusion criteria were women who had a previous or planned CS, as in many instances IOL may be contraindicated or there is planned management of a CS.

Clinical predictors of interest were pre-specified using existing evidence from national1 and local clinical guidelines and expert opinion. These were categorized into (i) social and demographical characteristics of women (e.g. age, marital status and area of residence), (ii) complications in previous pregnancies [e.g. pre-eclampsia (PET), stillbirth and postpartum haemorrhage (PPH)], (iii) existing medical morbidities (e.g. cardiovascular, endocrine and renal disorders) and (iv) complications in the current pregnancy [including antepartum haemorrhage (APH) and cholestasis of pregnancy].

The primary outcome measure was IOL, so a binary (dependent) variable was created for onset of labour (spontaneous/induced). Statistical analyses were undertaken using SPSS 13.1 for Windows. The Pearson chi-square ({chi}2) test and univariate logistic regression analysis tested associations between case mix factors and IOL. These results are expressed in unadjusted odds ratios (OR) and 95% confidence intervals (CI). Factors significant at one-way analysis (P < 0.05) were then entered into a multiple logistic regression model using forward stepwise technique to identify those factors predictive (independently associated) of IOL. The results of these analyses are presented as adjusted ORs and 95% CIs. The percentage correct estimated the extent to which the observed rate of IOL is explained by case mix factors in the model.

This study was given complete ethical approval by the local NHS Research Ethics Committee.


   Results
TOP
 Introduction
 Methods
 Results
Discussion
 Conclusions
 Funding
 Acknowledgements
 References
 
A total of 20 627 cases were extracted from the AMND and the overall IOL rate varied from 27.9% to 30.4% between 1999 and 2003. Of the 17 736 eligible births included in this study, spontaneous labour occurred in 12 009 (67.7%) cases and in 5727 (32.3%) cases labour was induced. Table 1 shows the results of the tests of crude association with social and demographical characteristics of women and onset of labour.


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  		Table 1 Social and demographical characteristics of women by onset of labour

 
The crude association of existing maternal medical morbidities, complications in previous pregnancies and complications in current pregnancies with onset of labour are shown in Table 2. Maternal conditions of urinary tract infection (UTI), epilepsy, depression, thrombosis, anxiety and endocrine disorders, such as hypothyroidism and diabetes mellitus were all significantly associated with IOL. Major obstetric complications in previous pregnancies such as abruption, major PPH or PET were not significantly associated with IOL in the index pregnancy. Considering obstetric complications in current pregnancies, all were significantly associated with an increased likelihood of IOL except assisted conception, chorioamnionitis and threatened miscarriage.


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  		Table 2 Pre-existing and existing maternal morbidity, complications in previous pregnancies and complications in current pregnancies by onset of labour

 
The 18 predictive factors independently associated with IOL are featured in Table 3. The five case mix factors in bold: PROM, polyhydramnios/oligohydramnios, PET, prolonged pregnancy (pregnancy gestation ≥41 weeks) and IOL in a previous pregnancy explained the majority of the variance of IOL. Notably, further independent predictors of IOL include demographic characteristics such as body mass index (BMI) >35 and residing in areas with intermediate or long distances and travel times from hospital. Morbidities such as epilepsy, thrombosis, nephropathy and hypothyroidism were not predictive of IOL.


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  		Table 3 Multivariate logistic regression model of socio-demographical and clinical characteristics predictive of induction of labour

 
All other current pregnancy complications associated with IOL using one-way analysis remained independently associated with IOL in the model. The model explained 71.5% (percentage correct) of the observed variation in IOL rate.


   Discussion
TOP
 Introduction
 Methods
 Results
 Discussion
Conclusions
 Funding
 Acknowledgements
 References
 
Main findings of this study
We found that nearly three-quarters of the observed rate of IOL could be explained by the socio-demographic and clinical characteristics of women, including well-recognized and expected clinical indicators for this intervention. However, more than one-quarter of IOL remains unexplained by women's case mix factors. It is this proportion of clinical activity that is likely to be explained by variations in women's preferences for care, clinicians' practice and organizational factors.

What is already known about this topic?
IOL for reasons other than prolonged pregnancy, PROM and diabetes mellitus are unsupported in the recently reviewed national evidence-based guidelines.1 They encourage clinicians to be cautious about performing IOL for other indications as this may be associated with an increased likelihood of CS and assisted vaginal delivery. However, most clinicians' have experienced women's requests for IOL and other obstetric interventions in the absence of recognized clinical indicators (elective IOL), or have been faced with pregnant women who suffer from what are classified as minor disorders in pregnancy, such as symphysis pubis dysfunction, and many may be sympathetic about this.

What this study adds?
Clearly, this study was not designed to test the effectiveness of IOL in relation to specific indicators; rather its aim was to explore and quantify how much of the observed rates of IOL can be explained by case mix factors. This study not only identifies the predictive factors of IOL, but goes on to estimate the extent to which case mix factors can explain the observed rate of IOL.

The five factors that explain the majority of the observed rate of IOL are well-recognized and clinically plausible indications for IOL. The use of IOL in the management of PROM and prolonged pregnancy is supported by the evidence-based national clinical guidelines1 as has been found to reduce the incidence of perinatal morbidity and mortality.11,12 PET and abnormal liquor volumes (polyhydramnios/oligohydramnios) are associated with perinatal morbidity and mortality, and its association with IOL is almost certainly a reflection of the clinical management of these diseases to improve outcomes.18 This study also suggests that clinicians may use interventions, like IOL, in subsequent pregnancies to try and reduce the risk of re-occurrence of complications, such as stillbirth.

Like other obstetric interventions, we found a disproportionate rate of IOL among pregnant women with a BMI >35. This study adds to the growing body of evidence that suggests that obese women are at increased risk of obstetric intervention.19,20 Even when controlling for potential confounders such as diabetes and PET, the association with IOL remained. An independent association was found between living in a residential area with an intermediate or long distance and travel time from hospital and IOL, with the likelihood increasing the greater the distance from home to hospital. This may be a result of clinicians' taking account of travel time to and from hospital in the clinical management of women. With further centralization of maternity services in the UK and concerns about equity and access in healthcare, this needs further exploration.21

The inclusion of UTI in the model is interesting and suggests an independent association with IOL, rather than it being indicative. Furthermore, if a history of mental illness (anxiety and depression) has been diagnosed by a physician and is recorded in the clinical records, then this is coded in the AMND. We found no other study that has reported a similar independent association between mental illness (anxiety and depression) and IOL. Anxieties about childbirth has been cited as contributing to the increased incidence of requests for CS in the absence of clinical indicators, therefore IOL may be performed for similar reasons. One notable finding is the association between a previously induced labour and IOL in a subsequent pregnancy remains even when controlling for previous or present maternal medical conditions. Although we cannot exclude the possibility that the association may be explained by co-morbidities that are not represented in this detailed data, it may also reflect women's choice and clinicians' preferences about the start of their labour.

With nearly three-quarters of the variation of IOL explained by case mix factors, this study clearly validates appropriate activity clinically. However, despite detailed analyses of these data, 28.5% of IOL cannot be explained by the socio-demographic and clinical characteristics of women. This proportion of IOL may account for the variation of induction rates between hospitals and clinicians' despite the use of clinical guidelines.1,4 Contributing factors that have been suggested are that the acceptability and perceived efficiency of prostaglandins and defensive practice among clinicians', have lessened the threshold for IOL.22 Organizational factors, such as the type and size of hospital, availability of neonatal facilities and workforce issues have been identified as contributing to other rising obstetric intervention rates and may also affect clinical decisions about the timing of labour.4

Limitations of this study
Limitations to this study include that despite extensive socio-demographic and clinical data detailing of each case, further factors may remain untested. For example, American studies suggest that ethnicity is associated with IOL.23 These descriptors were not available from the AMND data set, therefore were not factored into the analyses. During the period of the study, ethnic minority groups were estimated at only 4.7% of the residing population of Aberdeen city, so it is unlikely that this would contribute much to this study.24 Also, unreliable antenatal recording of intrauterine growth restriction and macrosomia in the AMND resulted in using a proxy measure. When serial antenatal scans (two or more scans at ≥24 weeks gestation) were performed in the absence of maternal morbidity, this was used as a marker for abnormal foetal growth.

This study uses data from one clinical setting, which raises questions about the generalizability of the findings. As a tertiary maternity hospital, AMH is not dissimilar to other units in the UK. It serves a population with varying clinical characteristics residing in diverse settings with varying distance and travel time to the tertiary hospital. The IOL rate for 2005 was 23% at AMH, which is very similar to the Scottish and English average at 24% and 20%, respectively.3,25 Therefore, it is reasonable to suggest that these findings may be applicable to similar populations served by other tertiary units in the UK.

Although this was an observational study using routinely collected clinical data. The strengths of the AMND data are that they are entered in a consecutive case series by trained and experienced coders. International Classification of Diseases Version 10 is used to code morbidity and clinical events. There are rigorous systems in place to ensure the reliability and validity of AMND data.


   Conclusions
TOP
 Introduction
 Methods
 Results
 Discussion
 Conclusions
Funding
 Acknowledgements
 References
 
Whereas the majority of the observed rate of IOL may be appropriately determined by clinical indications, the remainder is likely to be attributable to variation in obstetricians' practice and women's preferences for care. Existing policy drivers of evidence-based practice, promoting normality and patient-centred care that encourages the involvement of women as partners in their healthcare may each have conflicting impact on IOL rates. Future work exploring both women's and clinicians' preferences for care and decision-making about IOL may give further insight beyond case mix factors that might influence rates of IOL. This information is necessary if effective interventions are to be developed that ensure that IOL is appropriate and acceptable to women.


   Funding
TOP
 Introduction
 Methods
 Results
 Discussion
 Conclusions
 Funding
Acknowledgements
 References
 
This study was funded by the Nursing, Midwifery and Allied Health Professions (NMAHP) Research Training Scheme (Scottish Executive, Health Foundation and NHS Education Scotland).


   Acknowledgements
TOP
 Introduction
 Methods
 Results
 Discussion
 Conclusions
 Funding
 Acknowledgements
References
 
The authors would like to thank Dr Campbell and staff at the AMND for facilitating the data extraction.

Conflict of interest. Neither of the authors declare any conflicts of interest.


   References
TOP
 Introduction
 Methods
 Results
 Discussion
 Conclusions
 Funding
 Acknowledgements
 References
 

  1. National Institute for Health and Clinical Excellence. Induction of Labour. NICE Clinical Guidelines 70 (2008) London: National Collaborating Centre for Women's and Children's Health.
  2. Martin JA, Hamilton BE, Sutton PD, et al. Births: Final data for 2003 (2005) United States of America: National Vital Statistics Reports.
  3. National Health Services Scotland (NHS). Information Services (2005) Edinburgh: Scottish Health Statistics. www.isd.org/isd/files/mat_bb_table5.xls.
  4. Thomas J, Paranjothy S. The National Sentinel Caesarean Section Audit Report (2001) England: Clinical Effectiveness Unit, Royal College of Obstetricians and Gynaecologists.
  5. Joseph KS, Young DC, Dodds L, et al. Changes in maternal characteristics and obstetric practice and recent increases in primary caesarean delivery. Obstet Gynecol (2003) 102(4):791–800.[CrossRef][Web of Science][Medline]
  6. Australian Institute of Health and Welfare. National Perinatal Statistics Unit Reports. www.npsu.unsw.edu.au/Publications.htm (31 October 2006, date last accessed).
  7. Kaufman KE, Bailit JL, Grobman W. Elective induction an analysis of economic and health consequences. Am J Obstet Gynecol (2002) 187(4):858–63.[CrossRef][Web of Science][Medline]
  8. Scottish Executive Health Department. A Framework for Maternity Services in Scotland (2001) Edinburgh: Scottish Executive.
  9. National Services Framework for Children Young People and Maternity Services. Maternity Matters: Choice, Access and Continuity of Care in a Safe Service (2004) London: Department of Health.
  10. Scottish Government Health Department. Keeping Childbirth Natural and Dynamic (KCND) Programme (2008) Edinburgh: Scottish Government. www.scotland.gov.uk/Topics/Health/NHS-Scotland/nursing/naturalchildbirth.
  11. Hannah ME, Hannah WJ, Hellman J, et al. Induction of labour as compared with serial antenatal monitoring in post-term pregnancy. N Engl J Med (1992) 326:1587–92.[Abstract]
  12. Hannah ME, Ohlsson A, Farine D, et al. Induction of labor compared with expectant management for prelabor rupture of membranes at term. N Engl J Med (1996) 334:1005–10.[Abstract/Free Full Text]
  13. Kjos SL, Henry OA, Montoro M, et al. Insulin-requiring diabetes in pregnancy: a randomized trial of active induction of labor and expectant management. Am J Obstet Gynecol (1993) 169(3):611–5.[Web of Science][Medline]
  14. Cammu H, Martens G, Ruyssinck G, et al. Outcome after elective labor induction in nulliparous women: a matched cohort study. Am J Obstet Gynecol (2002) 186(2):240–4.[CrossRef][Web of Science][Medline]
  15. Maslow AS, Sweeney AL. Elective induction of labor as a risk factor for cesarean delivery among low-risk women at term. Obstet Gynecol (2000) 95(6):917–22.[CrossRef][Web of Science][Medline]
  16. World Health Organisation. International Statistical Classification of Diseases and Related Health Problems. (1992) Geneva: World Health Organisation. 9th Revision (ICD-9).
  17. Bhattacharya S, Campbell D. The incidence of severe complications of pre-eclampsia. Hypertens Pregnancy (2005) 24:181–90.[CrossRef][Web of Science][Medline]
  18. Roberts JM, Pearson G, Cutler J, et al. Summary of the NHLBI working group on research on hypertension during pregnancy. Hypertension (2003) 41:437–45.[Abstract/Free Full Text]
  19. Declercq E, Menacker F, MacDorman M. Maternal risk profiles and the primary cesarean rate in the United States, 1991–2002. Am J Public Health (2006) 96:867–72.[Abstract/Free Full Text]
  20. Kiran UTS, Hemmadi S, Bethel J, et al. Outcome of pregnancy in a woman with an increased body mass index. Br J Obstet Gynaecol (2005) 112:768–72.
  21. Tucker J, Hundley V, Kiger A, et al. Sustainable maternity services in remote and rural Scotland? A qualitative survey of staff views on required skills, competencies and training. Qual Saf Health Care (2005) 14:34–40.[Abstract/Free Full Text]
  22. Rayburn WF, Zhang J. Rising rates of labor induction: present concerns and future stategies. Obstet Gynecol (2002) 100:164–7.[CrossRef][Web of Science][Medline]
  23. Coonrod D, Bay RC, Kishi GY. The epidemiology of labor induction: Arizona, 1997. Am J Obstet Gynecol (2000) 182:1355–62.[CrossRef][Web of Science][Medline]
  24. Kelso L, Walsh D, Whyte B. Aberdeen Central. A Community Health and Well-being Profile (2004) Glasgow: NHS Health Scotland.
  25. The Information Centre. NHS Maternity Statistics 2004-05 (2005) England: The Information Centre, Community Health Statistics.

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This Article
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