, Volume 21, Issue 10, pp 721–735 | Cite as

Consumer Preference for Dinoprostone Vaginal Gel Using Stated Preference Discrete Choice Modelling

Original Research Article


Objective: To assess consumer preference for two methods of induction of labour using stated preference discrete choice modelling. The methods of induction were artificial rupture of the membranes (ARM) plus oxytocin and dinoprostone (prostaglandin E2) vaginal gel, followed by oxytocin if necessary.

Methods: Consumer preference was measured in terms of willingness to pay for each of the attributes. These attributes were the method of administration, place of care, length of time from induction to delivery, need for epidural anaesthetic, type of delivery and cost. Levels were assigned to each of the attributes. Pregnant women attending a public hospital antenatal clinic were asked to read a description of the two methods and then to choose between them in 18 different scenarios in which the levels of the attributes were varied.

Results: Women were willing to pay 11 Australian dollars ($A) for a 1% reduction in the chance of needing oxytocin as well as the gel and $A55 for every 1 hour reduction in the length of time from induction to delivery. For a 1% reduction in the chance of needing an epidural anaesthetic or Caesarean section, women expressed a willingness to pay of $A20 and $A90, respectively. All estimates were obtained in 1998 and expressed in Australian dollars ($A1 = $US0.63).

Conclusion: Women valued the less invasive method of administration of the gel and the associated greater freedom of movement during labour. However, they valued the shorter time from induction to delivery associated with ARM plus oxytocin more highly. A policy which allows women access to the gel for up to two doses would accommodate this consumer preference.


Caesarean Section Oxytocin Discrete Choice Contingent Valuation Consumer Preference 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



We are very grateful to Dr Andrew Child, the Director of Obstetrics and Gynaecology at King George V Hospital, Sydney for his support for this project. In addition, we would like to thank Mai Lee, the Nursing Unit Manager of the Outpatient Clinics, and her staff for their co-operation during the data collection period in the clinics. Finally we would like to thank the Pharmacy Board of NSW for their financial assistance.

This study was supported financially by the Pharmacy Board of NSW. However, there were and are no conflicts of interest relevant to the content of this manuscript.


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Copyright information

© Adis Data Information BV 2003

Authors and Affiliations

  1. 1.Faculty of PharmacyUniversity of SydneySydneyAustralia

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