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Journal of Public Health Advance Access published online on January 13, 2009
Journal of Public Health, 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


  Abstract

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


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